Integrated BIM-Based Simulation of Energy, Photovoltaic, and Thermal Performance in Residential Buildings

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Ebere Donatus Okonta, Cherunjeet Kumar Mondal, Oluwafemi Ajayi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9450013/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract The increasing demand for low-carbon and energy-efficient residential buildings has intensified the need for integrated performance assessment frameworks that can simultaneously evaluate energy consumption, renewable energy potential, and indoor thermal conditions. Conventional building energy studies often rely on isolated simulation approaches, limiting the ability to capture the interdependencies between building systems, solar resources, and occupant comfort. This gap is particularly evident in small-scale residential developments, where practical, simulation-ready workflows remain underdeveloped. This study proposes a BIM-enabled integrated simulation framework for assessing energy demand, photovoltaic (PV) potential, and thermal performance in residential buildings. A detailed multi-zone building model was developed from an as-designed BIM model and implemented in TRNSYS, incorporating construction properties, occupancy schedules, internal heat gains, and local climatic data for Guisborough, United Kingdom. The framework integrates energy modelling, solar irradiation analysis, and HVAC system simulation within a unified workflow. The results demonstrate that the building maintains stable indoor thermal conditions within comfort ranges (20–24.3°C) throughout the year while exhibiting a dominant heating demand of 33.2 MWh and minimal cooling requirements. The integration of a 33-module PV system generates approximately 9.7 MWh annually, covering up to 60% of the building’s electricity demand and achieving a seasonal energy surplus during summer months. The system contributes to an estimated annual reduction of 2.2 tCO₂ emissions. This study is original in using a fully detailed BIM-derived model without geometric simplification to enable realistic, reproducible multi-domain assessment. Unlike conventional methods, it integrates energy, solar, and thermal analysis within a practical workflow for early-stage design and small-scale residential buildings. Building Information Modelling (BIM) Building Energy Simulation Photovoltaic Systems (PV) Thermal Comfort Residential Buildings Integrated Simulation Framework Energy Performance Assessment Carbon Emissions Reduction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. INTRODUCTION The escalating effects of climate change, together with rapid urbanisation and rising energy demand, have heightened the necessity for sustainable and low-carbon development in the built environment (Agboola et al., 2023). The construction industry continues to be one of the foremost consumers of global energy, representing between 36–40% of overall energy consumption and a similar proportion of carbon dioxide (CO₂) emissions globally (European Commission, 2019; International Energy Agency, 2022). In this sector, residential structures constitute a substantial portion of operational energy demand due to constant occupancy, heating needs in temperate areas, and increasing dependence on electrical appliances (Ruellan et al., 2016; González-Torres et al., 2022). Therefore, strengthening the energy and environmental efficiency of residential structures is essential for attaining global net-zero carbon objectives and promoting urban sustainability (Okonta & Rahimian, 2024). Building energy simulation has become an essential instrument for assessing building performance; it facilitates the forecasting of energy demand, indoor thermal conditions, and system behaviour across diverse operational and climatic scenarios (Di Stefano et al., 2023; Crawley et al., 2001). Historically, these assessments have depended on physics-based simulation engines, such as EnergyPlus and TRNSYS, which accurately mimic heat transfer processes, HVAC system functionality, and occupant interactions with outstanding temporal precision. Although these tools have markedly improved building performance assessment, their use has frequently been confined to single-domain analysis, wherein energy consumption, renewable energy potential, and indoor environmental quality are regarded as separate evaluation streams (Jiang et al., 2024). Building performance is intrinsically multi-faceted and interrelated (de Wilde, 2019). Solar radiation concurrently affects photovoltaic (PV) power production, indoor thermal gains, and cooling requirements (Ramos et al., 2017; Herrando & Ramos, 2022). Similarly, building exterior features affect both thermal comfort and energy consumption, whereas occupancy behaviour governs internal heat gain and system operation (Ghorbani Naeini et al., 2024). The fragmentation of these areas in traditional studies limits the capacity to capture such interactions, leading to incomplete or potentially deceptive performance evaluations (Allegrini et al., 2015; Reinhart and Cerezo Davila, 2016). Recent research increasingly highlights the necessity of integrated modelling methodologies that account for these interdependencies within a cohesive analytical framework (Kaviani et al., 2023). In this context, Building Information Modelling (BIM) has evolved as a revolutionary digital technology that facilitates comprehensive performance evaluation (Uduokhai et al., 2023). BIM offers a data-intensive, parametric depiction of building geometry, materials, and spatial relationships, promoting seamless interchange with simulation tools and enhancing data-driven decision-making throughout the construction lifecycle (Eastman et al., 2011; An et al., 2024). BIM functions as a centralised information repository, facilitating the direct transfer of geometric and material data into simulation environments, minimising modelling redundancy, and enhancing accuracy. This feature is especially advantageous in performance-orientated design, where swift iteration and feedback are crucial (An et al., 2024; Gerrish et al., 2017). Notwithstanding these achievements, numerous significant research gaps persist. Numerous BIM-based studies persist in concentrating on singular-domain evaluations, such as energy modelling or solar analysis, without achieving the comprehensive integration of numerous performance domains into a unified workflow (Sola et al., 2018). This constrains the capacity to assess trade-offs and synergies among energy demand, renewable energy production, and interior environmental conditions. Secondly, a significant amount of current research depends on simplified or abstracted building geometries, frequently sourced from GIS datasets or archetype models, which overlook intricate construction characteristics and diminish the realism of simulation results (Reinhart and Cerezo Davila, 2016; Nageler et al., 2018). Third, there is a significant deficiency of research utilising integrated BIM-enabled simulation frameworks for small-scale residential projects that employ fully detailed, as-designed models. The majority of current research concentrates on extensive urban districts or optimisation-centric scenarios, which may not accurately represent realistic design limitations or real-world implementation circumstances (Mutani and Todeschi, 2020). The interoperability between BIM and simulation systems continues to present major obstacles, including data loss, geometric discrepancies, and the necessity for model reduction during data exchange (Kamel and Memari, 2019; O’Donnell et al., 2013). These constraints underscore the necessity for resilient, reproducible methods that preserve geometric integrity while facilitating multi-domain performance evaluation. Confronting these problems is crucial for transforming BIM from a design documentation instrument to a comprehensive operational performance analysis platform in the realm of sustainable and intelligent urban development. These constraints prompt a basic inquiry in research: How can a BIM-enabled integrated simulation framework be developed and applied to simultaneously evaluate energy demand, photovoltaic potential, and indoor thermal performance in residential buildings using detailed, as-designed models? Addressing the question is crucial for enhancing performance-based design techniques that are both pragmatic and relevant to actual residential constructions. This study proposes a BIM-enabled integrated simulation framework to evaluate energy demand, solar potential, and indoor thermal performance in residential buildings. The proposed system integrates dynamic thermal modelling, solar irradiation analysis, and renewable energy modelling into a cohesive workflow, in contrast to traditional methods that address these topics independently. The research used a comprehensive multi-zone model directly sourced from an as-designed BIM model, guaranteeing that the analysis accurately represents the building's geometry, material characteristics, and operational conditions. The main objective of this study is to assess the integrated energy, solar, and thermal performance of a residential building through a BIM-based simulation methodology. To accomplish this objective, the study pursues three distinct aims: (i) to quantify the building's annual and seasonal energy requirements, encompassing heating, cooling, and electricity usage; (ii) to evaluate the solar energy potential and photovoltaic (PV) electricity production based on rooftop attributes and climatic factors; and (iii) to analyse indoor thermal performance across various zones to ascertain adherence to thermal comfort standards. This work is significant for its contribution to the advancement of integrated performance evaluation approaches in the built environment. This research establishes a replicable and simulation-ready BIM-enabled workflow, connecting architectural design with performance evaluation and facilitating informed decision-making in the early design phases. The study offers empirical insights into the integration of renewable energy and energy-efficient design solutions in residential buildings, facilitating the transition to low-carbon dwelling. The suggested paradigm ultimately enhances the creation of sustainable, resilient, and energy-efficient built environments by facilitating a comprehensive assessment of building performance across several domains. 2. LITERATURE REVIEW 2.1 Building and District Energy Modelling Approaches Energy modelling in the built environment has progressed from rudimentary estimation methods to sophisticated dynamic simulation systems (Di Stefano et al., 2023). Initial methodologies, including degree-day and bin methods, offered swift assessments of heating and cooling requirements; nevertheless, they were constrained by steady-state assumptions and a limited capacity to depict occupancy, control tactics, and transient thermal dynamics. In contrast, comprehensive physical simulation techniques employ thermodynamic and thermal balancing principles to accurately depict envelope behaviour, HVAC functionality, internal gains, and zone-level interactions (Röpke & De Marco, 2023). These methodologies are currently extensively utilised in simulation software including EnergyPlus, DOE-2, and TRNSYS (Crawley et al., 2001). On a larger scale, urban and district building energy models have expanded these methodologies beyond single structures to encompass neighbourhoods and groups of buildings. Top-down models, dependent on aggregated statistical data, are beneficial for policy-level analysis but possess limited utility for design-phase decision-making due to their absence of geometric and material specificity (Swan and Ugursal, 2009). Bottom-up models offer enhanced spatial and physical detail by depicting individual structures or archetypes and consolidating their outcomes. This method is better suitable when form, orientation, shading, and envelope attributes significantly affect performance results (Reinhart & Cerezo Davila, 2016). Recent research has sought to enhance the robustness of district-scale modelling by integrating physics-based simulations with calibrated or data-driven approaches. Hybrid modelling methodologies can enhance reliability while reconciling data requirements and computational demands (Nageler et al., 2018). Nonetheless, the literature persists in demonstrating a conflict between modelling intricacy and practical utility. Simplified models enhance speed at the expense of realism, while comprehensive models necessitate lengthy preprocessing, additional assumptions, and greater computational resources. This trade-off is especially crucial for residential structures and small-scale projects, where design-specific attributes greatly influence performance and where generic archetypal assumptions may be overly simplistic (Fakhari et al., 2026). In the current work, precise physical simulation represents the most pertinent modelling paradigm. The study aims to evaluate a particular residential structure based on its as-designed geometry, construction characteristics, occupancy patterns, HVAC assumptions, and rooftop photovoltaic arrangement. The significance of the literature is not in examining every energy modelling tradition, but in illustrating why dynamic simulation provides the most suitable methodological basis for integrated building-level evaluation. 2.2 BIM-to-Simulation Workflows in Evaluating Building Performance BIM has progressively been established as a pivotal data environment for performance-oriented design. BIM functions not merely as a geometric representation tool; it also stores and organises information regarding building components, materials, thermal zones, and spatial relationships, thus facilitating interoperability with simulation platforms (Ahsan, 2024). BIM has been particularly advantageous in energy and environmental studies, as the laborious recreation of geometry and construction data in distinct simulation tools has traditionally led to inefficiencies and inaccuracies. The primary advantage of BIM-to-simulation processes is the minimisation of redundancy and the enhancement of consistency between design intent and analytical models. Researchers have demonstrated that BIM can enable data transfer to simulation engines via interoperable formats like IFC and gbXML, facilitating thermal and environmental assessments derived from a common digital model (Alexandrou et al., 2023). This enhances the viability of preliminary performance evaluation and facilitates iterative design testing. Nonetheless, research clearly indicates that BIM-to-simulation interoperability is far from effortless. Data transmission often entails semantic loss, erroneous material allocation, insufficient zone definitions, and geometric discrepancies that necessitate rectification prior to the execution of credible simulations (Jia et al., 2026). Consequently, numerous researchers endorse semi-automated methods wherein BIM models are enhanced or reorganised prior to their application in analytical simulations (Cann et al., 2022; Roman et al., 2023). These issues become more obvious when extending beyond single-building applications or when attempting to maintain geometric integrity for simultaneous solar and energy analysis. This body of literature is pertinent to the current investigation. While BIM facilitates an integrated modelling logic, the practical workflow frequently relies on intermediary processing processes to convert architectural geometry into a simulation-ready model. This issue is significant not only from a technical standpoint; it directly influences reproducibility, dependability, and the credibility of simulation results. The literature indicates that a BIM-enabled study contributes not merely through the use of a digital model, but by illustrating a definitive and practical pathway from the design model to the analytical model. 2.3 Comprehensive Evaluation of Energy, Solar, and Thermal Efficacy A primary restriction in traditional building performance research is the inclination to analyse energy consumption, solar resource availability, and thermal behaviour as distinct domains (Jurjevic & Zakula, 2023). In practice, these variables are intricately interrelated. Solar radiation impacts indoor heat gains, influences heating and cooling demands, and concurrently dictates the power output of rooftop solar systems (Mustafa et al., 2024). Similarly, envelope parameters, glazing properties, and building orientation affect both operating energy requirements and indoor thermal conditions. Assessing these categories in isolation may thus obscure significant trade-offs and synergies. The literature on urban and building simulation has progressively acknowledged the necessity for integration. Hardy et al. (2024) note that simulation tools have evolved to possess multi-domain capabilities; yet, their implementation is still scattered across many platforms and processes. Wolk and Reinhart (2025) observe that several urban building energy studies depend on simplified geometric abstractions, which are effective for large-scale mapping but inadequate when it is essential to maintain design-specific correlations between form, construction, and performance. Ziaeemehr et al., 2023 further illustrate that ambient climatic factors and solar exposure can significantly influence building energy consumption, suggesting that performance indicators should not be analysed in isolation. In residential applications, integrated assessment is crucial as heating demand, internal gains, and rooftop PV performance are significantly affected by the same design and operational variables (Custódio et al., 2022). However, a significant portion of the literature continues to be segmented among studies concentrating on operational energy, solar mapping, and comfort or ambient factors in isolation. Salom et al. (2021) contend that neighbourhood and building energy modelling should progressively transition towards integrated performance analysis, while Bourdic and Salat (2012) and Kong et al. (2023) similarly emphasise the drawbacks of disjointed modelling practices in urban-scale building energy research. Integration is crucial for this study, as the research aims to quantify energy consumption and evaluate the interaction between solar generation and interior thermal performance inside a single household system. The research thus advocates for a methodology that assesses these categories concurrently rather than sequentially or independently. 2.4 Precision, Simplification, and Reproducibility in Simulation-Based Research A persistent challenge in building performance research is the equilibrium between model realism and computational feasibility. High-fidelity dynamic models may accurately depict intricate envelope assemblies, occupancy schedules, HVAC controls, and renewable systems, although they necessitate comprehensive data preparation and meticulous model configuration (Kim et al., 2022). In contrast, simpler or archetype-based models can be implemented swiftly and extensively; nevertheless, they can obscure essential variations in building geometry, materiality, and usage patterns (Ali et al., 2019). This challenge is exacerbated with BIM-enabled simulation, as the complexity of design models does not inherently ensure their preparedness for simulation. Construction-focused BIM models may include superfluous geometric detail or non-analytical components that obstruct effective energy modelling, whereas excessive simplification can undermine the inherent benefits that BIM is intended to provide (Rossi, 2025). Consequently, the literature increasingly advocates for modelling methodologies that retain critical physical details while ensuring analytical feasibility. Reproducibility constitutes a significant concern. Cornaro et al. (2023) observe that the comparability of simulation research is frequently diminished by ambiguous assumptions, proprietary preprocessing methods, and inadequately disclosed model changes. In BIM-enabled performance research, repeatability relies on both the selection of the simulation engine and the clarity of the workflow employed to generate analytical models from design models. This is especially crucial for small-scale residential studies, where detailed design information is significant and the modelling approach must remain applicable for practical use. The relevance for the current study is unequivocal: a valuable contribution entails not merely simulating a structure, but executing this through a transparent and reproducible workflow that accurately reflects realistic geometry, material characteristics, operational schedules, and renewable energy systems. This situates the research within a significant methodological discourse on the utilisation of intricate digital building models, avoiding both excessive simplification and analytical unwieldiness. 2.5 Research Gaps and Contextualisation of the Current Study The examined literature identifies multiple gaps pertinent to this study. Many current research continue to utilise a disjointed analytical framework, evaluating operational energy demand, solar potential, and thermal behaviour in isolation, despite the physical interdependence of these factors (Delzendeh et al., 2017; Shaker et al., 2024). This constrains the capacity to comprehend integrated performance results within a singular building system. A significant portion of urban and district energy studies depends on archetype models, GIS-based abstractions, or simplified massing representations, which are effective for large-scale analysis but lack the necessary detail for accurate building-level evaluation (Cerezo Davila, 2017; Deng et al., 2022). Such simplifications frequently overlook envelope details, glazing properties, operational timelines, and design-specific solar interactions, all of which are crucial to residential performance. Third, despite BIM being extensively advocated as a facilitator for performance-based design, the literature consistently highlights enduring interoperability issues between design models and simulation environments, such as data loss, semantic inconsistency, and the necessity for manual refinement prior to analysis (Tolk, 2024; Tolk, 2018). This indicates that numerous asserted BIM-enabled workflows continue to be challenging to replicate or implement in practice. Fourth, research remains restricted in illustrating how a comprehensive, as-designed residential model can facilitate an integrated and practically repeatable evaluation of energy consumption, photovoltaic generation, and indoor thermal efficiency within a single workflow. A significant portion of the current research concentrates on extensive urban-scale investigations, optimization-centric scenarios, or singular domain evaluations, hence creating a void in the assessment of realistic residential performance (Mirabella et al., 2018; Yu & Fang, 2023). This study addresses the existing gap. Instead of engaging in extensive urban modelling or abstract optimisation, the study formulates a BIM-enabled simulation workflow focused on a particular residential building and assesses three interconnected areas: energy demand, rooftop photovoltaic potential, and internal thermal performance. This technique enhances the realism and methodological transparency of integrated residential building assessment. The research indicates that building performance simulation has significantly advanced, especially through dynamic modelling tools and the increasing importance of BIM in facilitating analytical processes. Nonetheless, considerable obstacles persist concerning integration, interoperability, model realism, and reproducibility. Current research frequently divides energy, solar, and thermal studies, depends on oversimplified models that diminish practical applicability, or lacks adequately transparent BIM-to-simulation pathways for comprehensive home applications. This study addresses a specific methodological requirement: the creation of an integrated and reproducible BIM-enabled workflow for assessing energy demand, photovoltaic efficiency, and indoor thermal conditions in a realistic residential structure. The subsequent chapter delineates the approach employed to implement this paradigm via a comprehensive case-study model and dynamic simulation environment. 3. METHODOLOGY 3.1 Research Design and Modelling Framework This study adopts a simulation-based, BIM-enabled analytical research design to evaluate the integrated energy, solar, and thermal performance of a residential building. The methodological approach is grounded in the principle that building performance is a multi-domain phenomenon, where energy demand, renewable energy generation, and indoor environmental conditions are intrinsically interdependent. Consequently, rather than applying isolated simulation techniques, this research develops a unified framework that integrates these domains within a single computational workflow. The framework is structured around three core components: (i) BIM-based geometric and material data extraction, (ii) dynamic thermal and energy simulation, and (iii) solar irradiation and photovoltaic (PV) performance analysis. The BIM model serves as the central data repository, enabling consistent transfer of building geometry, construction properties, and spatial zoning into the simulation environment. This ensures that the analysis reflects the actual design configuration rather than simplified abstractions. The simulation workflow follows a sequential yet interconnected process, beginning with model development and parameter definition, followed by system integration within the simulation engine, and culminating in performance evaluation across energy consumption, thermal comfort, and renewable energy generation, as seen in Fig. 1 . This integrated design enables the capture of cross-domain interactions, particularly the influence of solar radiation on both PV output and indoor thermal behaviour. 3.2 Case Study Description and BIM Model Development The case study comprises a residential apartment located in Guisborough, United Kingdom, selected to represent a typical small-scale housing development within a temperate climatic context. The building consists of a two-level apartment with a total gross floor area of approximately 389.5 m² and a south-orientated primary façade, allowing for meaningful evaluation of solar exposure and passive heating potential. The initial architectural model was developed in Autodesk Revit, which was used for the initial architectural model, while SketchUp was used to refine the geometry for surface consistency, zoning clarity, fenestration definition, and simulation compatibility before transferring the model into the simulation environment, as seen in Fig. 2 . The building was subdivided into six thermal zones, each representing distinct functional spaces within the apartment, enabling detailed analysis of spatial variations in thermal behaviour. Construction assemblies were modelled using layered material definitions to accurately represent thermal transmittance properties. The external wall system, for instance, consists of multiple layers, achieving a U-value of 0.240 W/m²K, while double-glazed window systems with a U-value of 1.1 W/m²K and solar transmittance of 0.62 were incorporated to capture both insulation performance and solar heat gain characteristics. The building was divided into six thermal zones (A–F) to capture spatial variations in occupancy, solar exposure, and internal heat gains, as seen in Fig. 3 . This zoning strategy enables a more accurate representation of thermal behaviour compared to single-zone models. Table 1 summarises the distribution of building area and gross floor area across the defined six (6) thermal zones. Zone F contains by far the largest share of space, with a building area of 201.71 m² and a gross floor area of 121.35 m², making it the dominant zone in the overall layout. Zones E and B also account for substantial portions, recording building areas of 85.79 m² and 79.87 m², respectively, with gross floor areas of 77.15 m² and 79.97 m². In contrast, Zone C has the smallest allocation, with only 27.62 m² of building area and 15.25 m² of gross floor area. The total building area across all zones is 501.10 m², while the total gross floor area is 389.48 m². This indicates that the larger proportion of the building mass and usable floor space is concentrated mainly in Zones F, E, and B, whereas Zones A, C, and D contribute comparatively smaller shares. Table 1 Distribution of building area and gross floor area across thermal zones. Zone Building area (m²) Gross floor area (m²) A 61.91 51.56 B 79.87 79.97 C 27.62 15.25 D 44.20 44.2 E 85.79 77.15 F 201.71 121.35 TOTAL 501.10 389.48 The building envelope was defined based on typical UK residential construction practices, incorporating multi-layered wall, roof, floor, and ceiling assemblies with specified material properties and thicknesses. These constructions were modelled to accurately represent heat transfer through the building fabric in the simulation environment. The building uses layered construction elements with substantial insulation across the envelope. As seen in Table 2 , the floor is the thickest element at 450 mm, followed by the external wall at 367.5 mm and the roof at 352.5 mm, showing that thermal protection is concentrated mainly in the ground, wall, and roof assemblies. The ceiling is moderately thick at 247.5 mm, while the internal load wall is the thinnest at 125 mm and is primarily for structural division rather than insulation. The material build-ups combine plaster, masonry, concrete, timber, and insulation to provide structural strength and improved thermal performance, with mineral wool and rigid insulation playing a key role in reducing heat transfer through the building envelope. Table 2 Construction details of building envelope elements, including material composition and layer Element Thickness Materials (outside–>inside) External wall 367.5 mm Plaster 12.5 mm + Brick 102.5 mm + insulated cavity mineral wool 140 mm + concrete block 100 mm + plaster 12.5 mm Internal load wall 125 mm Plaster 12.5 mm + concrete block (100 mm) + plaster 12.5 mm Ceiling 247.5 mm Plaster – 12.5 mm + timber joists – 100 mm + Mineral wool insulation above joists – 135 mm Roof 352.5 mm Roof tiles (clay or concrete) – 15 mm + Timber battens – 25 mm + Rafters – 150 mm + Insulation (mineral wool or PIR) – 150–mm + Plaster 12.5 mm Floor 450 mm Sand 25 mm + Compacted hardcore (Crushed stone) 150 mm + Reinforced concrete slab 100 mm+ Rigid insulation (PIR/EPS) 100 mm + Cement screed 65 mm + Floor finish (tiles/wood/carpet) 10 mm 3.3 Simulation Environment and Model Implementation The building model was implemented in TRNSYS 18, a dynamic simulation platform widely used for transient energy system analysis. The simulation environment enables the integration of multiple system components, including thermal zones, weather data processing, ground temperature modelling, and renewable energy systems within a unified computational structure. The multi-zone thermal simulation was conducted using Type 56, which models heat transfer processes within and between zones based on energy balance equations. Weather data for the simulation was derived from Meteonorm, ensuring location-specific climatic inputs, including solar radiation, ambient temperature, and humidity profiles. Ground temperature effects were incorporated using Type 77, while photovoltaic system performance was simulated using Type 103 components. The simulation was executed with an hourly time step over a full annual cycle (8,760 hours), allowing for high-resolution temporal analysis of building performance. Initial indoor conditions were defined to reflect standard comfort assumptions, ensuring consistency across simulation runs. The simulation scheme implemented in TRNSYS is shown in Fig. 4 . The central component is Type 56, which performs the multi-zone thermal simulation of the apartment. This component is supported by Type 15, which reads the TMY weather file, and Type 77, which models the ground temperature. Component Type 103b is used to simulate the two PV arrays installed on the building. In addition, the simulation includes plotters, integrators, and data printers for monitoring, processing, and recording the simulation results. 3.4 Operational Parameters and Boundary Conditions To ensure realistic representation of building operation, detailed boundary conditions were defined, including occupancy schedules, internal heat gains, ventilation rates, and HVAC control strategies. Occupancy-driven schedules were applied to lighting, appliances, and system operation, reflecting typical residential usage patterns over a 24-hour cycle. Figure 5 illustrates the daily operational profiles adopted in the simulation, including (a) occupancy and lighting, (b) appliance usage, and (c) HVAC and ventilation control. These schedules govern the temporal distribution of internal gains and system operation, thereby influencing both thermal behaviour and energy demand. Internal heat gains from occupants, equipment, and lighting were estimated based on standard guidelines and uniformly distributed across thermal zones. Air infiltration and ventilation rates were set at 0.3 h⁻¹ and 0.4 h⁻¹, respectively, aligning with typical residential ventilation practices and system operation schedules. Thermal comfort was maintained through a heat pump-based HVAC system, with heating and cooling setpoints defined at 21°C and 25°C, respectively, alongside setback temperatures during unoccupied periods. These control strategies were implemented to reflect energy-efficient operation while maintaining acceptable indoor environmental conditions. Table 3 presents the elements considered as heat gain sources, together with their corresponding total thermal power values. Appendix B provides a room-by-room breakdown of the assumed household appliances, including their electrical consumption and associated heat generation according to ASHRAE Handbook [ASHRAE]. In the model, the specific internal gains (per unit floor area) are assumed to be uniformly distributed across all thermal zones Table 3 Internal heat gain assumptions for the building model Item Heat gain (kJ/h·m²) Description Occupancy 8.41 7 persons Appliances 50.14 Typical appliances for a single-family dwelling in the UK Lights 3.60 LED lamps Total, specific internal heat gain 62.15 Combined internal heat gain from occupancy, appliances, and lighting Gross Floor Area (m²) 389.48 Total, Floor area of the building model 3.5 Building Envelope and Photovoltaic System Modelling The renewable energy component of the study focuses on the integration of rooftop PV systems within the building model. Two PV arrays were installed on available roof surfaces, with a total of 33 modules distributed across different roof orientations and inclinations. The PV modules were modelled based on manufacturer specifications, including efficiency, nominal power output, and collector area. Solar irradiation on roof surfaces was dynamically calculated using weather data inputs, enabling accurate estimation of incident solar energy and resulting electricity generation. The modelling approach accounts for geometric factors such as roof orientation, tilt angle, and available surface area, which directly influence solar energy capture. This integration allows for simultaneous assessment of solar resource availability and its contribution to building energy demand reduction. Table 4 Key thermal and optical properties of the modelled external window Parameter Value Unit Window dimensions 0.77 × 1.08 m U-value 1.10 g-value 0.62 – Spacer type Insulated – Frame fraction 0.15 – Frame c-value 8.17 kJ/h·m²K Solar absorptance 0.60 – Emissivity 0.90 – Internal convective coefficient 11 kJ/h·m²K External convective coefficient 64 kJ/h·m²K As seen in Table 4 , the external window used in the simulation was modelled as an insulated glazing system with dimensions of 0.77 m by 1.08 m. The glazing properties were defined with a U-value of 1.1 W/m²K and a solar heat gain coefficient (g-value) of 0.62, indicating a thermally efficient window with moderate solar transmittance. An insulated spacer was also specified in the glazing assembly to improve thermal performance and reduce heat loss around the glass edges. In addition to the glazing properties, the frame characteristics were included in the model to provide a more realistic representation of window heat transfer. The frame fraction was set at 0.15, meaning that 15% of the total window area was occupied by the frame. The frame thermal parameter (c-value) was defined as 8.17 kJ/h·m²K, with solar absorptance and emissivity values of 0.6 and 0.9, respectively. Convective heat transfer coefficients were specified as 11 kJ/h·m²K for the indoor surface and 64 kJ/h·m²K for the outdoor surface. Together, these parameters enabled the simulation to capture both conductive and solar heat transfer effects through the window system more accurately. 3.6 Performance Evaluation Metrics The performance of the building was evaluated using a set of quantitative indicators that capture energy demand, renewable energy generation, thermal comfort, and environmental impact. Energy performance was assessed through annual heating and cooling demand, as well as total electricity consumption from HVAC systems, appliances, and lighting. Photovoltaic performance was evaluated based on annual electricity generation and solar fraction, defined as the proportion of building energy demand met by solar energy. Additionally, carbon emission reductions were estimated using national emission factors, allowing for assessment of environmental benefits associated with renewable energy integration. Thermal performance was analysed through indoor air temperature profiles across all thermal zones, ensuring compliance with defined comfort thresholds. The integration of these metrics provides a comprehensive understanding of building performance across multiple domains, enabling robust evaluation of design effectiveness. 4. RESULTS 4.1 Surface Solar Irradiation Profile Figure 6 shows the hourly incident solar radiation on the south-facing wall, north-facing wall, and 8° sloped roof over the 8,760-hour simulation period. Under the adopted simulation conditions, solar exposure is clearly non-uniform across the building envelope, confirming that radiation intensity is strongly controlled by orientation and surface inclination. This result is significant because it demonstrates that the building does not operate under a homogeneous irradiance field; instead, different envelope elements are subjected to markedly different solar regimes, with direct consequences for both passive heat gains and active solar energy generation. The sloped roof consistently records the highest solar exposure throughout the year, with substantially greater and more sustained peaks than either of the wall surfaces. This confirms that the roof is the dominant solar collection surface and therefore the most effective location for photovoltaic deployment. Its superior performance is especially evident during spring and summer, when higher solar altitude and longer daylight duration amplify incident radiation. In analytical terms, this establishes the roof as the principal driver of the building’s active solar energy potential. The wall surfaces, by contrast, exhibit lower and more orientation-dependent exposure. The north-facing wall remains the least irradiated surface across almost the entire year, indicating minimal direct solar contribution and limited relevance for useful passive gains. The south-facing wall performs more favourably, particularly during winter, when it provides stronger solar gains than the north façade. This is a critical result because it suggests that the south-facing façade has greater passive thermal relevance during periods of peak heating demand. Although it does not rival the roof in annual solar capture, its wintertime performance indicates a more strategic role in supporting passive solar heating. This distinction between roof-dominated annual capture and south-façade winter responsiveness is important for interpreting the integrated building performance. The roof primarily supports active solar conversion through PV electricity generation, whereas the south wall contributes more selectively to seasonal thermal gains. The solar profile therefore, reveals that different building surfaces support different energy functions, rather than contributing equally to the building’s overall performance. The annual irradiation values for the roof surfaces of Zones A and F are both approximately 991 kWh/m², indicating that the two PV locations receive almost identical solar resources. This is analytically important because it rules out solar availability as the main cause of later differences in PV output. Instead, the subsequent variation in electricity generation must be attributed primarily to system-related factors, particularly installed module capacity and collector area. The results demonstrate that solar exposure across the building envelope is both surface-specific and seasonally differentiated. This non-uniformity is not merely descriptive; it underpins the building’s later thermal and photovoltaic behaviour and confirms the importance of evaluating solar incidence as a distributed performance variable rather than a single building-wide average. 4.2 Indoor Thermal Conditions Across Thermal Zones Indoor temperatures remained within a comparatively narrow range throughout the annual cycle, despite substantial seasonal variation in external climatic conditions. As illustrated in Fig. 7 , the building interior did not passively track outdoor temperature fluctuations; instead, the thermal behaviour of the six zones remained closely regulated around the prescribed comfort thresholds. This indicates that the combined effect of the building envelope, internal gains, and HVAC control strategy was sufficient to dampen the large external temperature swings and maintain stable indoor conditions across the year. The contrast between external and internal temperature behaviour is particularly pronounced. Outdoor air temperature exhibited wide annual variation, dropping below 0°C during colder periods and rising to approximately 25°C in summer, whereas the indoor zones remained much more stable. Ground temperature followed a smoother and more attenuated seasonal pattern, yet it still varied more than the internal zone temperatures. As shown in Fig. 9 , this strong thermal damping confirms that the apartment functioned as a thermally moderated system, where the internal environment was buffered against short-term and seasonal outdoor variability. However, thermal stability at whole-building level did not imply complete uniformity across the six zones. Distinct spatial differences remained evident, particularly during the warmer months. Zone A consistently recorded the highest temperatures and showed the strongest summer elevation, while Zone C remained comparatively cooler throughout much of the year. This pattern suggests that local thermal conditions were influenced by zone-specific factors such as solar exposure, glazing, internal heat gains, and spatial configuration. The result is therefore not simply one of stable indoor temperature, but one of controlled yet differentiated thermal response within the same dwelling. This intra-building variation becomes clearer when examined through the monthly averages presented in Table 5 . Although indoor temperatures were generally maintained within a comfort-oriented range of approximately 20.0°C to 24.3°C, the spread between zones remained noticeable. Zone A reached a monthly average of 24.3°C in July, whereas Zone C recorded 21.7°C in the same month, giving a difference of 2.6°C. This is analytically significant because it demonstrates that identical control settings did not produce identical thermal outcomes across spaces. Instead, the thermal response remained spatially uneven, even under an overall effective control regime. Table 5 Monthly average indoor air temperatures for the 6 thermal zones Month Air Zone Temperature [ºC] TA TB TC TD TE TF Avg Jan 20.0 20.0 20.0 20.0 20.1 20.1 20.0 Feb 20.2 20.0 20.0 20.1 20.2 20.2 20.1 Mar 20.6 20.2 20.1 20.2 20.4 20.4 20.3 Apr 21.0 20.5 20.3 20.4 20.7 20.7 20.6 May 22.1 21.0 20.5 20.7 21.0 21.0 21.1 Jun 23.2 21.9 20.8 21.3 21.8 21.6 21.8 Jul 24.3 23.2 21.7 22.4 23.1 22.6 22.9 Aug 23.9 22.8 21.4 22.0 23.0 22.2 22.5 Sep 22.7 21.5 20.8 21.2 22.1 21.3 21.6 Oct 21.0 20.7 20.5 20.6 21.0 20.7 20.7 Nov 20.4 20.2 20.2 20.3 20.4 20.3 20.3 Dec 20.0 20.0 20.0 20.1 20.1 20.1 20.1 Avg 21.6 21.0 20.5 20.8 21.2 20.9 21.0 Table 3 also shows that this pattern was not limited to a single month. Across the annual cycle, Zone A remained consistently warmer than the other zones, while Zones C and D generally occupied the lower end of the thermal range. The remaining zones fell between these extremes, indicating a gradation of indoor conditions rather than a uniform building-wide profile. This reinforces the evidence from Fig. 9 that the apartment achieved overall thermal control, but not complete thermal homogeneity. Taken together, Fig. 9 and Table 3 indicate that the building achieved two important outcomes simultaneously: seasonal thermal stability and zone-level thermal differentiation. The first confirms the effectiveness of the HVAC and envelope system in maintaining indoor comfort across changing external conditions, while the second reveals the continued influence of spatial and design-related factors on local thermal behaviour. This is a critical finding because it highlights the value of multi-zone analysis: a simplified single-zone model would likely have captured the general stability of the building, but it would have obscured the internal thermal contrasts that are clearly evident in both the hourly and monthly results. 4.3 Space-Conditioning Demand he space-conditioning results reveal a markedly unbalanced thermal demand profile in which heating overwhelmingly dominates annual building operation, while cooling remains limited in both magnitude and duration. As shown in Fig. 8 , the building requires heating across a substantial portion of the year, whereas cooling is confined to relatively short periods associated with warmer seasonal conditions. This indicates that the thermal performance of the apartment is fundamentally governed by winter heat losses and seasonal heating requirements rather than by persistent summertime overheating. The dominance of heating is evident not only in annual totals but also in the temporal structure of the load profile. Heating demand extends across most of the simulation year, with sustained peaks during the colder months and only a gradual decline toward summer. Even during transition periods, heating remains present, although at reduced intensity, indicating that the building continues to depend on active thermal input outside the core winter season. By contrast, cooling demand appears only intermittently and is concentrated within a narrow summer window. Its occurrence is sporadic rather than continuous, suggesting that internal temperatures exceed the cooling threshold only under specific short-term combinations of external temperature, solar gains, and internal loads. This asymmetry is confirmed quantitatively by the annual demand totals. Heating demand reaches 33.2 MWh, whereas annual cooling demand is only 0.36 MWh. The scale of this difference is analytically significant because it shows that cooling represents only a negligible fraction of the total space-conditioning requirement. In practical terms, the building operates as a heating-led system, with cooling contributing very little to annual energy use. The results therefore suggest that the building’s climatic and operational context is far more strongly shaped by the need to offset heat loss than by the need to manage prolonged internal overheating. The pattern observed in Fig. 10 also indicates that the imbalance between heating and cooling is not simply one of annual totals but of annual persistence. Heating is not only larger in magnitude; it is distributed over a much longer part of the year. This prolonged duration points to a building that experiences sustained seasonal demand for thermal support, even if indoor temperatures remain generally stable under the adopted control strategy. Cooling, in contrast, is both low-intensity and short-lived, implying that any overheating tendency is localised and episodic rather than systemic. This result is important when interpreted alongside the indoor temperature patterns discussed earlier. The fact that the building maintains relatively stable indoor conditions while still recording high annual heating demand indicates that thermal comfort is being achieved through continued active intervention rather than through passive thermal balance alone. At the same time, the very limited cooling requirement suggests that the building envelope and operational controls are broadly effective in preventing excessive summer heat accumulation at whole-building level, even though some zones show relatively higher temperatures during warmer months. The space-conditioning profile confirms that the apartment is strongly heating-dominated, with annual performance shaped primarily by cold-season energy demand. Figure 10 and the corresponding annual totals therefore establish a critical feature of the building’s operational behaviour: maintaining indoor comfort in this case depends far more on sustained heating provision than on cooling control. This has direct implications for subsequent interpretation of electricity demand, HVAC contribution, and the extent to which photovoltaic generation can offset overall operational energy use. 4.4 Photovoltaic Electricity Generation Photovoltaic generation followed a strongly seasonal pattern, with output rising progressively from winter into spring, peaking during the high-irradiance months, and then declining again toward the end of the year. As reflected in Fig. 9 , both roof-mounted arrays responded to the same broad annual solar cycle, but their electrical yields differed substantially in magnitude. This indicates that while seasonal irradiance governed the timing of PV production, system configuration determined the scale of electricity output. The annual pattern shows that PV generation was highly concentrated in spring and summer, when longer daylight hours and stronger solar irradiance increased electricity production. During winter, output from both arrays remained comparatively low, confirming that on-site solar generation made only a limited contribution during the same period when heating demand was highest. This seasonal mismatch is analytically important because it shows that the PV system did not provide a uniform contribution to building energy supply across the year; rather, its effectiveness was strongly dependent on the seasonal availability of solar resources. A clear performance imbalance was observed between the two arrays. The larger roof-mounted system consistently outperformed the smaller one across the entire simulation period, with no period in which their outputs converged meaningfully. Annual generation from the larger array reached approximately 8.0 MWh, whereas the smaller array produced about 1.7 MWh, giving a combined annual PV output of roughly 9.7 MWh. The difference is therefore not marginal or episodic, but structurally embedded in the system configuration. This distinction is critical in analytical terms because the two roof surfaces were shown earlier to receive almost identical annual irradiation, at approximately 991 kWh/m². The difference in electricity generation cannot therefore be attributed primarily to unequal solar resource availability. Instead, the evidence indicates that the gap in output was mainly capacity-driven, resulting from differences in installed collector area and module number rather than locational solar advantage. In practical terms, the higher-yielding array benefited from a much larger deployment scale, accommodating 27 modules compared with only 6 on the smaller array. The dominance of the larger array is therefore a function of system sizing rather than superior exposure conditions. This result has important implications for interpreting the PV contribution within the integrated energy framework. It shows that roof suitability alone does not determine electricity yield; the extent of usable area and the scale of module installation are equally decisive. Thus, although both roof sections were comparably favourable in solar resource terms, their contribution to building electricity supply was highly uneven because one was able to host substantially greater generating capacity. 4.5 Monthly Energy Balance and End-Use Distribution The monthly energy balance reveals a pronounced seasonal restructuring of the building’s energy system, driven by the inverse relationship between space-heating demand and photovoltaic generation. When the main operational components are considered together, as reported in Table 6, three patterns become evident: first, heating demand dominates the annual thermal profile; second, appliance and lighting loads remain comparatively stable throughout the year; and third, PV generation is highly seasonal and concentrated in the high-irradiance months. The significance of this integrated view is that it shows not only how much energy is consumed or produced, but also how the timing of demand and on-site generation shapes the building’s overall performance. Table 6. Monthly energy demand, electricity consumption, photovoltaic production, and efficiency-related outputs. kWh_th kWh kWh/m2 kWh - kWh - Tons Month Q_heat Q_cool Ele_app Ele_light H_roof_8 H_roof_5 PV_roof_8 PV_roof_5 PV_total COP HVAC HVAC_pow Consump Elect Solar F [-] tCO2 saved Jan 5742.3 0.0 812.3 168.8 20.1 19.7 151.9 34.6 186.5 3.0 1914.1 2895.1 0.06 0.04 Feb 5008.9 0.0 733.7 147.4 35.7 35.0 281.2 63.9 345.1 3.0 1669.6 2550.7 0.14 0.08 Mar 4346.8 0.0 812.3 161.6 76.6 74.4 611.8 140.2 752.0 3.0 1448.9 2422.8 0.31 0.17 Apr 2934.3 0.1 786.1 151.6 112.6 114.4 946.3 206.8 1153.0 3.0 978.2 1915.8 0.60 0.26 May 1565.9 7.7 812.3 152.0 155.4 154.5 1265.6 282.7 1548.3 3.0 524.5 1488.8 1.04 0.35 Jun 605.4 28.4 786.1 145.3 148.3 150.5 1219.3 266.4 1485.7 3.0 211.3 1142.7 1.30 0.33 Jul 163.2 188.1 812.3 150.4 150.2 151.5 1212.6 266.6 1479.1 3.0 117.1 1079.7 1.37 0.33 Aug 202.5 101.7 812.3 158.0 119.1 119.3 954.6 211.6 1166.2 3.0 101.4 1071.6 1.09 0.26 Sep 656.4 32.4 786.1 154.8 86.4 85.8 688.8 154.3 843.1 3.0 229.6 1170.4 0.72 0.19 Oct 2349.2 0.8 812.3 163.2 47.1 46.3 368.4 83.3 451.7 3.0 783.3 1758.8 0.26 0.10 Nov 4109.4 0.0 786.1 161.7 24.7 24.0 184.9 42.4 227.3 3.0 1369.8 2317.6 0.10 0.05 Dec 5573.0 0.0 812.3 168.8 15.5 15.4 115.0 25.8 140.8 3.0 1857.7 2838.7 0.05 0.03 Avg 2771.4 29.9 797.0 157.0 82.6 82.6 666.7 148.2 814.9 3.0 933.8 1887.7 0.59 0.18 TOTAL 33257.2 359.2 9563.9 1883.4 991.6 991.0 8000.1 1778.7 9778.8 11205.5 22652.8 2.2 The thermal demand profile remains strongly winter-oriented. Monthly heating demand is highest in January, December, and February, at 5742.3 kWh, 5573.0 kWh, and 5008.9 kWh respectively, before falling sharply toward summer. Cooling demand, by contrast, remains negligible for most of the year and becomes noticeable only during the warmest months, peaking at 188.1 kWh in July. This reinforces the earlier result that the building is fundamentally heating-led rather than cooling-led. However, the value of Table 4 lies in showing how this thermal behaviour translates into electricity demand through HVAC operation. With an average heat pump COP of 3.0, annual HVAC electricity consumption reaches 11,205.5 kWh, confirming that space conditioning constitutes the single largest electricity end use in the apartment. At the same time, non-HVAC electricity demand remains relatively stable across the year. Annual electricity use from appliances is 9563.9 kWh, while lighting accounts for 1883.4 kWh. Unlike heating demand, these loads show only modest monthly variation, which means that the building retains a consistent base electricity demand even when seasonal thermal loads decline. This stability is analytically important because it prevents summer electricity demand from falling in direct proportion to the reduction in heating load. In other words, the building’s annual electricity profile is shaped by the interaction between a variable HVAC load and a persistent plug-and-lighting base load. This interaction becomes more meaningful when compared with PV production. As shown in Table 4 and synthesised in Fig. 10 , PV generation increases from 186.5 kWh in January to a peak of 1548.3 kWh in May, remaining high through June and July before declining again into autumn and winter. The pattern is therefore highly seasonal and closely tied to irradiance availability. Annual PV production totals 9778.8 kWh, of which 8000.1 kWh is generated by the larger roof array and 1778.7 kWh by the smaller one. The key analytical point is that PV generation peaks precisely when heating-related electricity demand is at its lowest, producing a seasonal reversal in the balance between demand and on-site supply. This mismatch between seasonal demand and seasonal production is one of the most important results in the study. During winter, electricity consumption remains high because of sustained heating requirements, while PV output is minimal. In January, for example, total electricity consumption is 2895.1 kWh, compared with PV generation of only 186.5 kWh, giving a solar fraction of just 0.06. A similar pattern is observed in December and November, where solar fractions remain at 0.05 and 0.10, respectively. These figures indicate that the contribution of on-site solar generation to building operation is very limited during the months when energy demand is greatest. By contrast, the balance shifts substantially from late spring into summer. In April, the solar fraction rises to 0.60, indicating that PV generation covers 60% of electricity demand. From May onward, the relationship becomes even more pronounced: the solar fraction reaches 1.04 in May, 1.30 in June, 1.37 in July, and 1.09 in August. This means that during these months, on-site PV production is sufficient not only to offset the building’s electricity demand but also to exceed it. The transition from winter deficit to summer surplus, visible in Fig. 10 , demonstrates that the building does not experience a constant renewable contribution across the year; instead, it moves between two distinctly different operating states depending on season. This seasonal imbalance has direct implications for how the PV system should be interpreted. On an annual basis, on-site generation is substantial, but its usefulness is temporally uneven. The system performs strongly during months of low heating demand and weakly during months of high heating demand. As a result, annual totals alone risk overstating the functional alignment between renewable generation and building energy need. The monthly balance reported in Table 6 is therefore more informative than annual aggregation, because it reveals that the building’s apparent renewable performance depends heavily on when energy is required, not only on how much is produced over the year The end-use distribution further clarifies the structure of total annual electricity consumption. As summarised in Fig. 11 , HVAC accounts for approximately 49.5% of annual electricity demand, appliances contribute 42.2%, and lighting represents 8.3%. This disaggregation is analytically useful because it shows that although heating dominates the seasonal profile, the building’s annual electricity demand is not exclusively driven by HVAC. Appliances alone account for a very substantial share of consumption, almost approaching the HVAC contribution. This means that even if space-heating demand is reduced, a large non-thermal electricity load would remain. Lighting, by contrast, makes the smallest contribution and has relatively limited influence on the annual demand structure. Taken together, Table 6, Fig. 10 , and Fig. 11 show that the building’s energy behaviour is defined by a dynamic interaction between a highly seasonal heating load, a comparatively stable appliance-and-lighting base load, and a PV system whose output is concentrated in the high-irradiance months. The resulting pattern is neither balanced nor constant across the year. Instead, it is characterised by winter dependence on imported electricity and summer periods of strong solar offset and seasonal surplus. This integrated energy picture is critical because it moves beyond isolated reporting of demand or generation and shows how the building actually performs as a coupled thermal-electrical system over time. 4.6 Carbon Emissions Savings The carbon performance of the building was assessed by estimating the emissions avoided through the use of on-site photovoltaic electricity in place of grid-supplied electricity. The avoided emissions were calculated from the share of total monthly electricity demand met by PV generation, using the solar fraction as the key conversion term. As expressed in Eq. ( 1 ), the solar fraction was multiplied by the total monthly electricity consumption and the baseline grid emission factor for the UK electricity sector. $$\:{\text{CO}}_{2}{\text{ Saved (tCO}}_{2}\text{)}=\text{Solar fraction}\times \text{Total Elect (MWh)}\times {\text{Emission factor (tCO}}_{2}\text{/MWh)}$$ 1 A weighted average baseline emission factor of 0.22535 tCO₂/MWh was adopted for the UK power sector. On this basis, the avoided emissions profile closely follows the seasonal behaviour of photovoltaic generation rather than the overall electricity demand profile. This is analytically important because it shows that carbon savings are governed primarily by the timing of renewable electricity availability, not simply by the magnitude of annual consumption. The results indicate that avoided emissions remain low during winter, when solar generation contributes only a small fraction of total electricity demand. Monthly savings are 0.04 tCO₂ in January, 0.08 tCO₂ in February, 0.05 tCO₂ in November, and 0.03 tCO₂ in December, reflecting the weak contribution of PV during the same period in which heating-related electricity demand is highest. This confirms that the carbon reduction effect of the PV system is limited at the point in the year when grid dependence is greatest. By contrast, avoided emissions increase sharply from spring into summer as solar generation rises and the solar fraction improves. Monthly savings reach 0.17 tCO₂ in March, 0.26 tCO₂ in April, and 0.35 tCO₂ in May, before remaining high at 0.33 tCO₂ in both June and July. Although electricity demand is lower during these months, the much stronger PV contribution produces the greatest carbon benefit. This pattern demonstrates that the emissions reduction potential of the system is concentrated in the high-irradiance period rather than being evenly distributed across the annual cycle. This seasonal concentration is critical to the interpretation of the building’s environmental performance. On an annual basis, the PV system avoids approximately 2.2 tCO₂, confirming that on-site solar generation contributes a measurable reduction in operational carbon emissions. However, the monthly pattern shows that this benefit is temporally uneven and closely tied to the same winter-demand versus summer-generation mismatch observed in the energy balance analysis. In effect, the system delivers its strongest carbon savings when solar availability is highest, rather than when the building’s electricity dependence is greatest. This means that the annual carbon benefit is meaningful, but its practical effectiveness is moderated by the limited coincidence between peak renewable generation and peak energy demand. The carbon emissions results confirm that the photovoltaic system improves the environmental performance of the building, but they also expose the seasonal limitation of this benefit. The avoided emissions are substantial in annual terms, yet they are produced under conditions of uneven monthly alignment between renewable supply and building demand. This reinforces the broader result that annual sustainability gains should not be interpreted only in aggregate terms, since the temporal distribution of those gains is equally important in understanding actual building performance. 4.7 Synthesis of Key Quantitative Results Taken together, the results define a building with a strongly heating-dominated but thermally stable annual performance profile. Solar exposure was highest on the sloped roof, with both roof sections receiving approximately 991 kWh/m² annually, confirming their suitability for PV deployment, while the south-facing wall showed stronger winter solar gain than the north-facing wall. Indoor temperatures across the six zones remained within a controlled range of 20.0–24.3°C, although zone-level differences persisted, with Zone A consistently recording the highest temperatures and Zone C the lowest. Space-conditioning demand was overwhelmingly driven by heating, which reached 33.2 MWh annually, compared with only 0.36 MWh for cooling. This translated into an annual HVAC electricity use of 11.2 MWh, making HVAC the largest single electricity end use. Total annual electricity consumption was 22.6 MWh, comprising 11.2 MWh for HVAC, 9.6 MWh for appliances, and 1.8 MWh for lighting. The PV system generated 9.7 MWh annually, with the larger array contributing 8.0 MWh and the smaller array 1.7 MWh. Although both arrays received near-identical solar resources, the larger system dominated output because of its greater installed capacity. The seasonal energy balance showed low winter solar fractions of 0.05–0.14, rising to 0.60 in April and exceeding 1.0 between May and August, indicating seasonal electricity surplus during the high-irradiance period. This renewable contribution produced annual avoided carbon emissions of approximately 2.2 tCO₂. Monthly savings were lowest in winter and highest in late spring and summer, reaching about 0.35 tCO₂ in May and 0.33 tCO₂ in both June and July. Overall, the results show that the building achieves stable indoor comfort and meaningful renewable energy contribution, but that its operational performance remains strongly shaped by winter heating demand and the seasonal mismatch between peak demand and peak solar generation. 5. DISCUSSION 5.1 Heating-Dominated Energy Profile and Climatic Context The findings indicate that the building functions predominantly in a heating-dominated environment, exhibiting an annual heating demand of 33.2 MWh, contrasted with a mere 0.36 MWh for cooling. This significant disparity suggests that the primary operational problem in this climatic environment is not summer overheating, but the continuous necessity to counteract heat loss over an extended duration of the year. The building's energy performance is predominantly influenced by winter thermal vulnerability rather than cooling demand. This trend aligns with the climatic circumstances in Guisborough and, more generally, with residential structures in temperate countries, where moderate summer weather and prolonged cold seasons usually necessitate significantly higher heating than cooling demands (Reinhart & Cerezo Davila, 2016). The importance of the current result is not solely in affirming this general trend, but in demonstrating its robust persistence even within a structure that upholds generally steady indoor temperatures. The little cooling demand indicates that the envelope and management technique were generally successful in mitigating excessive summer heat accumulation at the whole-building level, albeit some localised warming in areas with increased sun exposure. The minimal cooling demand is especially indicative when analysed in conjunction with the indoor temperature outcomes. Despite the observation of elevated temperatures in Zone A throughout the summer months, these localised increases did not result in a significant annual cooling demand. This indicates that solar gains via exposed façades and glazing influenced regional variations in indoor temperature without fundamentally transitioning the structure into a cooling-dominant or mixed-demand regime. The implication is that passive solar impacts in this instance are more significant for local thermal variation than for annual cooling energy consumption. This outcome has two significant implications regarding performance. Initially, it suggests that energy efficiency measures in similar temperate-climate residential structures are expected to produce more significant advantages when focused on decreasing heating demand via enhanced insulation, airtightness, and control optimisation, rather than emphasising cooling reduction. Secondly, it indicates that annual building performance in these situations cannot be only assessed by total power consumption, as the primary factor influencing seasonal energy fluctuations is the demand for thermal cooling during winter operations. The result methodologically emphasises the significance of dynamic simulation. The great seasonal concentration of heating demand and the very limited, short-duration nature of cooling would be difficult to explain appropriately using simplistic steady-state techniques. The simulation accurately reflects the building's performance under real seasonal conditions by capturing hourly variations in climatic response, internal gains, and control behaviour. The findings consequently justify the continued use of comprehensive dynamic modelling methodologies, such as TRNSYS, for measuring climate-responsive residential energy performance (Crawley et al., 2001). 5.2 Indoor Thermal Stability and Spatial Variation The indoor thermal findings demonstrate that the structure attained a significant level of seasonal stability, with zone temperatures consistently held between 20.0–24.3°C despite considerable fluctuations in external conditions. This verifies that the synergistic effect of the envelope, internal gains, and HVAC management method successfully mitigated external climatic variations and sustained acceptable inside conditions year-round. The importance of this discovery resides not merely in the stability, but in its coexistence with distinct geographical diversity among zones. This finding aligns with other research indicating that well regulated HVAC systems can sustain constant indoor thermal conditions amidst fluctuating meteorological circumstances (Hensen and Lamberts, 2011). However, the current findings introduce a significant nuance: thermal regulation at the whole-building scale did not achieve thermal uniformity at the zone level. Zone A generally maintained higher temperatures than the other areas, especially throughout summer, whilst colder zones like Zone C displayed a more temperate reaction. This indicates that local elements, such as solar exposure, glazing attributes, and spatial arrangement, persistently influenced indoor conditions despite a uniform management system. The results correspond more closely with research that highlight the geographical variety of interior thermal behaviour. Allegrini et al. (2015) shown that solar exposure and local boundary conditions can markedly affect zone-level thermal response, even when buildings are assessed under a same operational framework. Likewise, extensive research on building performance has consistently demonstrated that single-zone models often diminish local temperature variations, perhaps exaggerating the homogeneity of indoor comfort levels. The current data substantiate that critique by revealing a quantifiable intra-building temperature disparity, especially during warmer months, despite the building's overall stability when assessed in aggregate terms. This comparison is analytically significant as it demonstrates that consistent average indoor circumstances should not be construed as proof of uniform thermal performance. The results thus underscore the significance of multi-zone simulation in evaluating home performance. Simplified models may suffice for general annual energy estimates, but they inadequately represent the spatially varied thermal behaviour essential for assessing comfort. The current work demonstrates that passive and active systems do not function separately; instead, their interaction results in a building that is thermally stable yet internally heterogeneous. This provides a more accurate depiction of residential performance and underscores the necessity of assessing indoor comfort not alone through annual averages, but also by considering zone-level variations. 5.3 Photovoltaic Performance and Seasonal Energy Dynamics The photovoltaic data indicate that annual generation was significant, albeit operationally inconsistent. The system generated a total photovoltaic output of 9.7 MWh, contributing significantly to the building's yearly electricity requirement of 22.6 MWh. Nonetheless, the more analytically significant finding is that this contribution exhibited considerable seasonality: generation peaked in spring and summer, while winter output remained subdued despite the period of highest heating-related demand. This indicates that annual PV yield, by itself, is an inadequate measure of operational efficiency, as it obscures the temporal discrepancy between renewable electricity supply and real building demand. This pattern aligns with prior research on PV-supported structures and urban energy systems in temperate regions. Sola et al. (2018) noted that while solar solutions can substantially enhance building energy performance, their efficacy is frequently limited by the seasonal disparity between maximum sun availability and peak thermal demand. Mutani and Todeschi (2020) shown that solar contributions in building and neighbourhood systems are significantly influenced by climatic seasonality, with peak renewable output occurring when space-heating demand is relatively low. The current data substantiate this interpretation: the minimal winter solar percentages and the summer excess from May to August do not signify an exceptional phenomenon of the case-study building, but rather indicate a fundamental constraint of rooftop photovoltaic systems in heating-dominated climates. The disparity between the two arrays elucidates the factors influencing PV yield in this instance. The significantly higher output from Array F cannot be attributed mainly to variations in solar resources, as both roof areas saw about comparable yearly irradiation. Instead, the finding verifies the claim by Jakubiec and Reinhart (2013) that PV power generation is determined not just by incident radiation, but also by geometric representation, usable roof area, and deployment arrangement. The predominance of Array F is thus most accurately understood as a capacity impact resulting from the quantity of modules and the collector area, rather than from any significant locational benefit in sun exposure. This distinction is significant, since it transitions the explanation from mere surface exposure to system scale and layout as the principal determinants of yield. The current results thus expand upon prior findings by demonstrating the operation of this seasonal mismatch inside a comprehensive BIM-enabled household model. Although the building demonstrates significant renewable energy contribution in summer and even generates a seasonal electrical surplus, it remains reliant on external electricity during winter. This reinforces the prevailing view in the literature that annual energy balance metrics may exaggerate the actual decarbonisation benefits of building-integrated photovoltaics if temporal supply-demand alignment is not taken into account (Sola et al., 2018; Mutani and Todeschi, 2020). The PV system has strong performance in annual aggregate metrics, although is less efficient when assessed in relation to the timing of real energy demand. This analysis further substantiates the assertion that photovoltaic integration alone is improbable to address the operational energy challenges of heating-dominant residential structures. Kaviani et al. (2023) assert that the efficacy of on-site renewables is enhanced when integrated with broader system methods, such storage, load shifting, or more adaptive energy management. In the context of this study, rooftop photovoltaic systems markedly enhance annual power performance; nonetheless, they do not eradicate winter grid need, as the building's peak demand coincides with periods of minimal solar generation. The primary result is that photovoltaic (PV) systems are advantageous, but their true efficacy is contingent upon the alignment of their temporal output profile with the seasonal demand patterns of the building. 5.4 Integrated Energy Balance and End-Use Distribution The integrated energy results indicate that HVAC constituted the predominant power end use, with about fifty percent of total annual consumption, while the balance was mostly linked to appliances and, to a lesser degree, lighting. This distribution aligns with prior home energy research in temperate settings, where space conditioning generally predominates operating power usage (Reinhart and Cerezo Davila, 2016). Nonetheless, the current findings indicate that non-HVAC loads continued to be sufficiently strong to maintain a significant base demand year-round, despite a reduction in heating demand. A significant discovery is that the link between demand and on-site generation was highly dynamic rather than static. The contribution of photovoltaic (PV) systems was constrained throughout winter, coinciding with peak electricity consumption, although experienced a significant increase in late spring and summer, resulting in intervals of seasonal surplus. This underscores the assertion by Sola et al. (2018) that annual energy totals alone may obscure significant temporal discrepancies between renewable generation and building demand. This analysis revealed that the structure exhibited varying performance throughout the year, transitioning from a winter deficit to a summer surplus. This underscores the significance of integrated simulation methodologically. This study assesses HVAC demand, end-use power, and PV output within a unified framework, thereby highlighting interactions frequently neglected when energy demand and renewable supply are examined independently. The findings advocate for more integrated performance assessment methodologies, especially for residential structures where seasonal demand-generation discrepancies significantly impact overall energy behaviour. 5.5 Carbon Emissions Reduction and Environmental Implications The yearly carbon reduction of roughly 2.2 tCO₂ demonstrates that rooftop photovoltaic systems can achieve a significant decrease in operational emissions in residential structures. This aligns with other research indicating that on-site renewable energy can significantly aid in operational decarbonisation, especially in contexts of considerable electricity demand (Mutani and Todeschi, 2020). In this instance, the importance of the outcome resides more in the irregular distribution of benefits throughout the year than in the annual total itself. The monthly trend indicates that emissions reduction closely aligned with photovoltaic generation, with the most significant savings observed in late spring and summer, and considerably lesser reductions during winter. This substantiates the assertion that annual carbon totals may exaggerate actual environmental advantages if the timing of renewable energy generation is disregarded. According to Sola et al. (2018), the significance of renewable contributions is contingent not only on the quantity of energy generated but also on the timing of such production in relation to demand from the building. The findings indicate that photovoltaic integration enhances environmental performance; nonetheless, it does not independently address the more profound decarbonisation challenge associated with heating-dominated buildings. According to Kaviani et al. (2023), enhanced carbon reduction is likely contingent upon the amalgamation of renewable energy generation, reduced heating demand, increased efficiency, and superior system integration. The consequence is that photovoltaic (PV) technology is a crucial decarbonisation strategy; nevertheless, its efficacy is contingent upon the seasonal disparity between supply and demand. 5.6 Global Implications for Temperate Climate Regions The implications of this study extend beyond Guisborough to include residential buildings in temperate marine and cool-temperate climates, covering substantial areas of Northern and Western Europe, select regions of North America, and particular sites in East Asia. In these settings, analogous environmental traits—mild summer conditions, lengthy heating seasons, and seasonally changing solar availability—typically give building performance patterns akin to those reported in our study. A primary finding is that household decarbonisation in temperate zones will primarily provide a heating problem rather than a cooling one. The considerable dominance of heating demand compared to cooling demand indicates that the most significant efficiency enhancements are expected to result from reducing heat loss through improved envelope design, insulation, airtightness, and control systems, rather than prioritising cooling-oriented solutions. This suggests that, under these circumstances, energy policy and design practice must prioritise heating reduction as the foremost strategy to diminish operating energy usage. The findings demonstrate that photovoltaic systems can significantly improve annual electricity generation; nonetheless, their role in temperate climates is mostly supplementary rather than entirely transformative when used in isolation. The photovoltaic system in this study substantially reduced annual electricity usage and generated a seasonal surplus in summer; however, its contribution during winter was negligible. This highlights a broader structural issue in temperate regions: the seasonal misalignment between the availability of renewable electricity and peak building demand. Thus, the effectiveness of photovoltaic deployment is maximised when assessed alongside storage, demand flexibility, and comprehensive grid integration, rather than as a standalone solution. A further notable implication relates to indoor environmental performance. The results demonstrate that stable indoor temperatures may be maintained despite varying external conditions; yet, localised thermal variations persist within the same dwelling. This is directly relevant to housing standards and performance-based regulation, as it suggests that adequate whole-building performance does not necessarily provide uniform comfort across all areas. In dwellings located in temperate climates, it is crucial to simultaneously evaluate energy efficiency and localised comfort. The study methodically demonstrates the importance of BIM-enabled integrated simulation for residential assessment in these regions. This method consolidates energy demand, solar potential, and indoor thermal performance into a cohesive framework, providing a more precise basis for decision-making during the design phase than fragmented or overly simplified approaches. This is especially relevant in temperate regions, where housing is often defined by small- to medium-scale developments, and where design-specific features can significantly influence performance outcomes. 6. CONCLUSION This study developed and applied a BIM-enabled integrated simulation framework for assessing the energy demand, photovoltaic potential, and indoor thermal performance of a residential building using a detailed as-designed model. The study responds to the persistent fragmentation in conventional building performance research, where energy, solar, and thermal analyses are often undertaken separately. By integrating these domains within a unified BIM-to-simulation workflow, the research demonstrates the value of a more holistic and practically reproducible approach to residential building performance assessment. The findings show that the case-study building exhibits a strongly heating-dominated annual performance profile. Annual heating demand reached 33.2 MWh, while cooling demand remained minimal at 0.36 MWh, confirming that building operation in this temperate context is governed primarily by winter heat loss rather than summer overheating. Despite this, indoor conditions remained comparatively stable across the six thermal zones, with temperatures generally maintained within a comfort range of about 20.0–24.3°C throughout the year. This indicates that the building envelope, internal gains, and HVAC control strategy were effective in maintaining overall thermal comfort, although noticeable zone-level differences persisted, particularly during warmer months. The results therefore highlight both the thermal resilience of the building and the importance of multi-zone analysis in revealing internal spatial variation that would be masked in simplified whole-building models. The solar and renewable energy results further demonstrate the benefit of integrated assessment. The roof surfaces received the highest incident solar radiation, confirming their suitability for photovoltaic deployment. The 33-module PV system generated approximately 9.7 MWh annually, with the larger array accounting for most of the total output due to its greater installed capacity. Although annual PV generation was substantial, its contribution was highly seasonal. Solar fractions remained low during winter, when heating demand and electricity dependence were highest, but increased significantly from spring to summer, exceeding 1.0 between May and August and indicating seasonal electricity surplus. This confirms that the building achieved meaningful on-site renewable contribution, but also reveals a strong temporal mismatch between peak energy demand and peak solar generation. On an annual basis, the system avoided approximately 2.2 tCO₂ emissions, demonstrating measurable environmental benefit, though this benefit was unevenly distributed across the year. Overall, the study confirms that a BIM-enabled integrated workflow can provide a realistic and robust basis for evaluating residential building performance across interconnected domains. The originality of the work lies not only in combining energy, solar, and thermal analyses, but in doing so through a fully detailed BIM-derived model without excessive geometric simplification. This enhances methodological transparency, preserves design fidelity, and improves the practical relevance of simulation outputs for early-stage decision-making and performance-informed residential design. Notwithstanding these contributions, the study has several limitations. First, the analysis is based on a single residential case study, which restricts the generalisability of the findings to other housing typologies, scales, or climatic contexts. Second, the simulation assumes fixed occupancy schedules and system operation patterns, which may not fully reflect the variability of real occupant behaviour. Third, the photovoltaic assessment does not incorporate energy storage or grid interaction strategies, both of which would influence the effective use of surplus summer electricity. In addition, the use of typical meteorological year data means that localised microclimatic effects and short-term weather extremes are not explicitly captured. Finally, although the study adopts a BIM-enabled workflow, the requirement for intermediate model refinement and processing indicates that full interoperability between design and simulation environments remains an unresolved challenge. Future research should therefore extend this framework beyond a single building to multiple-building or neighbourhood-scale applications in order to examine collective energy behaviour, distributed photovoltaic generation, and shared renewable infrastructure. Further work should also incorporate battery storage, demand response, and smart grid interactions to provide a more complete picture of operational energy flexibility. In methodological terms, improving BIM-to-simulation interoperability remains an important priority, especially through the development of more standardised and automated data exchange workflows that reduce manual preprocessing. Additional studies may also improve simulation realism by integrating measured occupancy patterns, monitored operational data, and higher-resolution climatic inputs. Such advances would strengthen the applicability of BIM-enabled integrated assessment as a practical tool for the design of low-carbon, energy-efficient, and climate-responsive residential buildings. Declarations Ethics approval and consent to participate - Not applicable. Consent for publication - All authors consent to the publication of this manuscript. Clinical trial number -Not applicable. Competing interests - The authors declare no competing interests Funding - This research did not receive any funding. Author Contribution Conceptualisation, methodology, formal analysis; Investigation, writing-review and editing- E.D.O, J.U; writing-review and editing, project administration, and Validation- C.K.M; J.U. writing-review and editing, project administration, and Validation, Investigation – O.A. All authors have read and agreed to publish this manuscript in your journal. Data Availability All data generated will be made available on reasonable request from the corresponding author. References Agboola, O. P., Ojobo, H., & Aliyev, A. (2023). Ameliorating climate change impacts on the built environment.Civil Engineering and Architecture, 11(3), 1324–1336 https://doi.org/10.13189/cea.2023.110317 . Ahsan, A. (2024). Integration of BIM and GIS for smart construction management (Master of Science dissertation, Politecnico di Torino). 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Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 14 May, 2026 Reviewers agreed at journal 14 May, 2026 Reviews received at journal 10 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers invited by journal 05 May, 2026 Editor assigned by journal 03 May, 2026 Submission checks completed at journal 19 Apr, 2026 First submitted to journal 17 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9450013","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":637783583,"identity":"59552972-215f-4cdd-a886-025dc036133a","order_by":0,"name":"1.\tEbere Donatus 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residential building subdivided into zones comprising the ground floor (top) and first floor (bottom) plans\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9450013/v1/fc422b86166232dcf7153397.jpeg"},{"id":109199045,"identity":"3af833bc-1988-46aa-b594-b073698cc61f","added_by":"auto","created_at":"2026-05-13 13:32:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":58250,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of the TRNSYS model used for building energy and solar PV simulation\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9450013/v1/cbc62b7d4b15c011d0553a99.png"},{"id":109205426,"identity":"ec257dee-8c1e-4fee-bcf2-71ca0172e4b4","added_by":"auto","created_at":"2026-05-13 15:04:44","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":483213,"visible":true,"origin":"","legend":"\u003cp\u003eDaily schedule profiles for a) occupancy and lights, b) appliances and c) HVAC and ventilation\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9450013/v1/822e2bafb680cdc57ab74ab4.jpeg"},{"id":109219653,"identity":"5470ad8d-9240-4b62-b824-c51914eaa6c1","added_by":"auto","created_at":"2026-05-13 19:58:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":244380,"visible":true,"origin":"","legend":"\u003cp\u003eSolar radiation over south walls (blue), north walls (green) and 8 deg slope rooftop (pink)\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9450013/v1/dd85e03aaae1792545e65920.png"},{"id":109219657,"identity":"94f607e0-7d6b-4379-a488-9d498fb74401","added_by":"auto","created_at":"2026-05-13 19:58:52","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":291251,"visible":true,"origin":"","legend":"\u003cp\u003eIndoor Air Temperatures of zones, outside air and ground temperatures\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9450013/v1/0c2120cd1b5356c2e539dda7.png"},{"id":109205244,"identity":"e3f5a396-196b-4a86-8234-9b53e6b87beb","added_by":"auto","created_at":"2026-05-13 15:03:52","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":160553,"visible":true,"origin":"","legend":"\u003cp\u003eHeating (pink) and cooling (green) demand throughout a year\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9450013/v1/c838d1f093b9f2c77a10085a.png"},{"id":109199047,"identity":"1d763dea-d5b4-4232-ad4d-e9aea3252f9b","added_by":"auto","created_at":"2026-05-13 13:32:30","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":165603,"visible":true,"origin":"","legend":"\u003cp\u003eEnergy outputs by PV system: array A (blue) and array F (red)\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-9450013/v1/dab6bb707c4ff36b9ac2c152.png"},{"id":109199050,"identity":"9d4230c2-2446-46fc-9645-a7f39999bdc2","added_by":"auto","created_at":"2026-05-13 13:32:30","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":78167,"visible":true,"origin":"","legend":"\u003cp\u003eEnergy demand and production throughout the year\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-9450013/v1/89d76565a21016d9ce37d14e.png"},{"id":109199048,"identity":"5971d7e5-95e4-435f-beea-09c1a3d12f44","added_by":"auto","created_at":"2026-05-13 13:32:30","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":59921,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAnnual contributions of HVAC, appliances, and lighting to total electricity consumption.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-9450013/v1/787df3205adcfea20a4c7998.png"},{"id":109249145,"identity":"eaecc958-ed80-4457-9854-8c24b6e10768","added_by":"auto","created_at":"2026-05-14 08:42:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3209529,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9450013/v1/34919615-b92d-48ac-879d-265642d7401f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated BIM-Based Simulation of Energy, Photovoltaic, and Thermal Performance in Residential Buildings","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe escalating effects of climate change, together with rapid urbanisation and rising energy demand, have heightened the necessity for sustainable and low-carbon development in the built environment (Agboola et al., 2023). The construction industry continues to be one of the foremost consumers of global energy, representing between 36\u0026ndash;40% of overall energy consumption and a similar proportion of carbon dioxide (CO₂) emissions globally (European Commission, 2019; International Energy Agency, 2022). In this sector, residential structures constitute a substantial portion of operational energy demand due to constant occupancy, heating needs in temperate areas, and increasing dependence on electrical appliances (Ruellan et al., 2016; Gonz\u0026aacute;lez-Torres et al., 2022). Therefore, strengthening the energy and environmental efficiency of residential structures is essential for attaining global net-zero carbon objectives and promoting urban sustainability (Okonta \u0026amp; Rahimian, 2024).\u003c/p\u003e \u003cp\u003eBuilding energy simulation has become an essential instrument for assessing building performance; it facilitates the forecasting of energy demand, indoor thermal conditions, and system behaviour across diverse operational and climatic scenarios (Di Stefano et al., 2023; Crawley et al., 2001). Historically, these assessments have depended on physics-based simulation engines, such as EnergyPlus and TRNSYS, which accurately mimic heat transfer processes, HVAC system functionality, and occupant interactions with outstanding temporal precision. Although these tools have markedly improved building performance assessment, their use has frequently been confined to single-domain analysis, wherein energy consumption, renewable energy potential, and indoor environmental quality are regarded as separate evaluation streams (Jiang et al., 2024).\u003c/p\u003e \u003cp\u003eBuilding performance is intrinsically multi-faceted and interrelated (de Wilde, 2019). Solar radiation concurrently affects photovoltaic (PV) power production, indoor thermal gains, and cooling requirements (Ramos et al., 2017; Herrando \u0026amp; Ramos, 2022). Similarly, building exterior features affect both thermal comfort and energy consumption, whereas occupancy behaviour governs internal heat gain and system operation (Ghorbani Naeini et al., 2024). The fragmentation of these areas in traditional studies limits the capacity to capture such interactions, leading to incomplete or potentially deceptive performance evaluations (Allegrini et al., 2015; Reinhart and Cerezo Davila, 2016). Recent research increasingly highlights the necessity of integrated modelling methodologies that account for these interdependencies within a cohesive analytical framework (Kaviani et al., 2023).\u003c/p\u003e \u003cp\u003eIn this context, Building Information Modelling (BIM) has evolved as a revolutionary digital technology that facilitates comprehensive performance evaluation (Uduokhai et al., 2023). BIM offers a data-intensive, parametric depiction of building geometry, materials, and spatial relationships, promoting seamless interchange with simulation tools and enhancing data-driven decision-making throughout the construction lifecycle (Eastman et al., 2011; An et al., 2024). BIM functions as a centralised information repository, facilitating the direct transfer of geometric and material data into simulation environments, minimising modelling redundancy, and enhancing accuracy. This feature is especially advantageous in performance-orientated design, where swift iteration and feedback are crucial (An et al., 2024; Gerrish et al., 2017).\u003c/p\u003e \u003cp\u003eNotwithstanding these achievements, numerous significant research gaps persist. Numerous BIM-based studies persist in concentrating on singular-domain evaluations, such as energy modelling or solar analysis, without achieving the comprehensive integration of numerous performance domains into a unified workflow (Sola et al., 2018). This constrains the capacity to assess trade-offs and synergies among energy demand, renewable energy production, and interior environmental conditions. Secondly, a significant amount of current research depends on simplified or abstracted building geometries, frequently sourced from GIS datasets or archetype models, which overlook intricate construction characteristics and diminish the realism of simulation results (Reinhart and Cerezo Davila, 2016; Nageler et al., 2018). Third, there is a significant deficiency of research utilising integrated BIM-enabled simulation frameworks for small-scale residential projects that employ fully detailed, as-designed models. The majority of current research concentrates on extensive urban districts or optimisation-centric scenarios, which may not accurately represent realistic design limitations or real-world implementation circumstances (Mutani and Todeschi, 2020).\u003c/p\u003e \u003cp\u003eThe interoperability between BIM and simulation systems continues to present major obstacles, including data loss, geometric discrepancies, and the necessity for model reduction during data exchange (Kamel and Memari, 2019; O\u0026rsquo;Donnell et al., 2013). These constraints underscore the necessity for resilient, reproducible methods that preserve geometric integrity while facilitating multi-domain performance evaluation. Confronting these problems is crucial for transforming BIM from a design documentation instrument to a comprehensive operational performance analysis platform in the realm of sustainable and intelligent urban development. These constraints prompt a basic inquiry in research:\u003c/p\u003e \u003cp\u003e \u003cem\u003eHow can a BIM-enabled integrated simulation framework be developed and applied to simultaneously evaluate energy demand, photovoltaic potential, and indoor thermal performance in residential buildings using detailed, as-designed models?\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAddressing the question is crucial for enhancing performance-based design techniques that are both pragmatic and relevant to actual residential constructions. This study proposes a BIM-enabled integrated simulation framework to evaluate energy demand, solar potential, and indoor thermal performance in residential buildings. The proposed system integrates dynamic thermal modelling, solar irradiation analysis, and renewable energy modelling into a cohesive workflow, in contrast to traditional methods that address these topics independently. The research used a comprehensive multi-zone model directly sourced from an as-designed BIM model, guaranteeing that the analysis accurately represents the building's geometry, material characteristics, and operational conditions. The main objective of this study is to assess the integrated energy, solar, and thermal performance of a residential building through a BIM-based simulation methodology. To accomplish this objective, the study pursues three distinct aims: (i) to quantify the building's annual and seasonal energy requirements, encompassing heating, cooling, and electricity usage; (ii) to evaluate the solar energy potential and photovoltaic (PV) electricity production based on rooftop attributes and climatic factors; and (iii) to analyse indoor thermal performance across various zones to ascertain adherence to thermal comfort standards.\u003c/p\u003e \u003cp\u003eThis work is significant for its contribution to the advancement of integrated performance evaluation approaches in the built environment. This research establishes a replicable and simulation-ready BIM-enabled workflow, connecting architectural design with performance evaluation and facilitating informed decision-making in the early design phases. The study offers empirical insights into the integration of renewable energy and energy-efficient design solutions in residential buildings, facilitating the transition to low-carbon dwelling. The suggested paradigm ultimately enhances the creation of sustainable, resilient, and energy-efficient built environments by facilitating a comprehensive assessment of building performance across several domains.\u003c/p\u003e"},{"header":"2. LITERATURE REVIEW","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Building and District Energy Modelling Approaches\u003c/h2\u003e \u003cp\u003eEnergy modelling in the built environment has progressed from rudimentary estimation methods to sophisticated dynamic simulation systems (Di Stefano et al., 2023). Initial methodologies, including degree-day and bin methods, offered swift assessments of heating and cooling requirements; nevertheless, they were constrained by steady-state assumptions and a limited capacity to depict occupancy, control tactics, and transient thermal dynamics. In contrast, comprehensive physical simulation techniques employ thermodynamic and thermal balancing principles to accurately depict envelope behaviour, HVAC functionality, internal gains, and zone-level interactions (R\u0026ouml;pke \u0026amp; De Marco, 2023). These methodologies are currently extensively utilised in simulation software including EnergyPlus, DOE-2, and TRNSYS (Crawley et al., 2001). On a larger scale, urban and district building energy models have expanded these methodologies beyond single structures to encompass neighbourhoods and groups of buildings. Top-down models, dependent on aggregated statistical data, are beneficial for policy-level analysis but possess limited utility for design-phase decision-making due to their absence of geometric and material specificity (Swan and Ugursal, 2009). Bottom-up models offer enhanced spatial and physical detail by depicting individual structures or archetypes and consolidating their outcomes. This method is better suitable when form, orientation, shading, and envelope attributes significantly affect performance results (Reinhart \u0026amp; Cerezo Davila, 2016).\u003c/p\u003e \u003cp\u003eRecent research has sought to enhance the robustness of district-scale modelling by integrating physics-based simulations with calibrated or data-driven approaches. Hybrid modelling methodologies can enhance reliability while reconciling data requirements and computational demands (Nageler et al., 2018). Nonetheless, the literature persists in demonstrating a conflict between modelling intricacy and practical utility. Simplified models enhance speed at the expense of realism, while comprehensive models necessitate lengthy preprocessing, additional assumptions, and greater computational resources. This trade-off is especially crucial for residential structures and small-scale projects, where design-specific attributes greatly influence performance and where generic archetypal assumptions may be overly simplistic (Fakhari et al., 2026). In the current work, precise physical simulation represents the most pertinent modelling paradigm. The study aims to evaluate a particular residential structure based on its as-designed geometry, construction characteristics, occupancy patterns, HVAC assumptions, and rooftop photovoltaic arrangement. The significance of the literature is not in examining every energy modelling tradition, but in illustrating why dynamic simulation provides the most suitable methodological basis for integrated building-level evaluation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 BIM-to-Simulation Workflows in Evaluating Building Performance\u003c/h2\u003e \u003cp\u003eBIM has progressively been established as a pivotal data environment for performance-oriented design. BIM functions not merely as a geometric representation tool; it also stores and organises information regarding building components, materials, thermal zones, and spatial relationships, thus facilitating interoperability with simulation platforms (Ahsan, 2024). BIM has been particularly advantageous in energy and environmental studies, as the laborious recreation of geometry and construction data in distinct simulation tools has traditionally led to inefficiencies and inaccuracies. The primary advantage of BIM-to-simulation processes is the minimisation of redundancy and the enhancement of consistency between design intent and analytical models. Researchers have demonstrated that BIM can enable data transfer to simulation engines via interoperable formats like IFC and gbXML, facilitating thermal and environmental assessments derived from a common digital model (Alexandrou et al., 2023). This enhances the viability of preliminary performance evaluation and facilitates iterative design testing.\u003c/p\u003e \u003cp\u003eNonetheless, research clearly indicates that BIM-to-simulation interoperability is far from effortless. Data transmission often entails semantic loss, erroneous material allocation, insufficient zone definitions, and geometric discrepancies that necessitate rectification prior to the execution of credible simulations (Jia et al., 2026). Consequently, numerous researchers endorse semi-automated methods wherein BIM models are enhanced or reorganised prior to their application in analytical simulations (Cann et al., 2022; Roman et al., 2023). These issues become more obvious when extending beyond single-building applications or when attempting to maintain geometric integrity for simultaneous solar and energy analysis. This body of literature is pertinent to the current investigation. While BIM facilitates an integrated modelling logic, the practical workflow frequently relies on intermediary processing processes to convert architectural geometry into a simulation-ready model. This issue is significant not only from a technical standpoint; it directly influences reproducibility, dependability, and the credibility of simulation results. The literature indicates that a BIM-enabled study contributes not merely through the use of a digital model, but by illustrating a definitive and practical pathway from the design model to the analytical model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Comprehensive Evaluation of Energy, Solar, and Thermal Efficacy\u003c/h2\u003e \u003cp\u003eA primary restriction in traditional building performance research is the inclination to analyse energy consumption, solar resource availability, and thermal behaviour as distinct domains (Jurjevic \u0026amp; Zakula, 2023). In practice, these variables are intricately interrelated. Solar radiation impacts indoor heat gains, influences heating and cooling demands, and concurrently dictates the power output of rooftop solar systems (Mustafa et al., 2024). Similarly, envelope parameters, glazing properties, and building orientation affect both operating energy requirements and indoor thermal conditions. Assessing these categories in isolation may thus obscure significant trade-offs and synergies. The literature on urban and building simulation has progressively acknowledged the necessity for integration. Hardy et al. (2024) note that simulation tools have evolved to possess multi-domain capabilities; yet, their implementation is still scattered across many platforms and processes. Wolk and Reinhart (2025) observe that several urban building energy studies depend on simplified geometric abstractions, which are effective for large-scale mapping but inadequate when it is essential to maintain design-specific correlations between form, construction, and performance. Ziaeemehr et al., 2023 further illustrate that ambient climatic factors and solar exposure can significantly influence building energy consumption, suggesting that performance indicators should not be analysed in isolation.\u003c/p\u003e \u003cp\u003eIn residential applications, integrated assessment is crucial as heating demand, internal gains, and rooftop PV performance are significantly affected by the same design and operational variables (Cust\u0026oacute;dio et al., 2022). However, a significant portion of the literature continues to be segmented among studies concentrating on operational energy, solar mapping, and comfort or ambient factors in isolation. Salom et al. (2021) contend that neighbourhood and building energy modelling should progressively transition towards integrated performance analysis, while Bourdic and Salat (2012) and Kong et al. (2023) similarly emphasise the drawbacks of disjointed modelling practices in urban-scale building energy research. Integration is crucial for this study, as the research aims to quantify energy consumption and evaluate the interaction between solar generation and interior thermal performance inside a single household system. The research thus advocates for a methodology that assesses these categories concurrently rather than sequentially or independently.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Precision, Simplification, and Reproducibility in Simulation-Based Research\u003c/h2\u003e \u003cp\u003eA persistent challenge in building performance research is the equilibrium between model realism and computational feasibility. High-fidelity dynamic models may accurately depict intricate envelope assemblies, occupancy schedules, HVAC controls, and renewable systems, although they necessitate comprehensive data preparation and meticulous model configuration (Kim et al., 2022). In contrast, simpler or archetype-based models can be implemented swiftly and extensively; nevertheless, they can obscure essential variations in building geometry, materiality, and usage patterns (Ali et al., 2019). This challenge is exacerbated with BIM-enabled simulation, as the complexity of design models does not inherently ensure their preparedness for simulation. Construction-focused BIM models may include superfluous geometric detail or non-analytical components that obstruct effective energy modelling, whereas excessive simplification can undermine the inherent benefits that BIM is intended to provide (Rossi, 2025). Consequently, the literature increasingly advocates for modelling methodologies that retain critical physical details while ensuring analytical feasibility. Reproducibility constitutes a significant concern. Cornaro et al. (2023) observe that the comparability of simulation research is frequently diminished by ambiguous assumptions, proprietary preprocessing methods, and inadequately disclosed model changes. In BIM-enabled performance research, repeatability relies on both the selection of the simulation engine and the clarity of the workflow employed to generate analytical models from design models. This is especially crucial for small-scale residential studies, where detailed design information is significant and the modelling approach must remain applicable for practical use.\u003c/p\u003e \u003cp\u003eThe relevance for the current study is unequivocal: a valuable contribution entails not merely simulating a structure, but executing this through a transparent and reproducible workflow that accurately reflects realistic geometry, material characteristics, operational schedules, and renewable energy systems. This situates the research within a significant methodological discourse on the utilisation of intricate digital building models, avoiding both excessive simplification and analytical unwieldiness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Research Gaps and Contextualisation of the Current Study\u003c/h2\u003e \u003cp\u003eThe examined literature identifies multiple gaps pertinent to this study. Many current research continue to utilise a disjointed analytical framework, evaluating operational energy demand, solar potential, and thermal behaviour in isolation, despite the physical interdependence of these factors (Delzendeh et al., 2017; Shaker et al., 2024). This constrains the capacity to comprehend integrated performance results within a singular building system. A significant portion of urban and district energy studies depends on archetype models, GIS-based abstractions, or simplified massing representations, which are effective for large-scale analysis but lack the necessary detail for accurate building-level evaluation (Cerezo Davila, 2017; Deng et al., 2022). Such simplifications frequently overlook envelope details, glazing properties, operational timelines, and design-specific solar interactions, all of which are crucial to residential performance. Third, despite BIM being extensively advocated as a facilitator for performance-based design, the literature consistently highlights enduring interoperability issues between design models and simulation environments, such as data loss, semantic inconsistency, and the necessity for manual refinement prior to analysis (Tolk, 2024; Tolk, 2018). This indicates that numerous asserted BIM-enabled workflows continue to be challenging to replicate or implement in practice. Fourth, research remains restricted in illustrating how a comprehensive, as-designed residential model can facilitate an integrated and practically repeatable evaluation of energy consumption, photovoltaic generation, and indoor thermal efficiency within a single workflow. A significant portion of the current research concentrates on extensive urban-scale investigations, optimization-centric scenarios, or singular domain evaluations, hence creating a void in the assessment of realistic residential performance (Mirabella et al., 2018; Yu \u0026amp; Fang, 2023). This study addresses the existing gap. Instead of engaging in extensive urban modelling or abstract optimisation, the study formulates a BIM-enabled simulation workflow focused on a particular residential building and assesses three interconnected areas: energy demand, rooftop photovoltaic potential, and internal thermal performance. This technique enhances the realism and methodological transparency of integrated residential building assessment.\u003c/p\u003e \u003cp\u003eThe research indicates that building performance simulation has significantly advanced, especially through dynamic modelling tools and the increasing importance of BIM in facilitating analytical processes. Nonetheless, considerable obstacles persist concerning integration, interoperability, model realism, and reproducibility. Current research frequently divides energy, solar, and thermal studies, depends on oversimplified models that diminish practical applicability, or lacks adequately transparent BIM-to-simulation pathways for comprehensive home applications. This study addresses a specific methodological requirement: the creation of an integrated and reproducible BIM-enabled workflow for assessing energy demand, photovoltaic efficiency, and indoor thermal conditions in a realistic residential structure. The subsequent chapter delineates the approach employed to implement this paradigm via a comprehensive case-study model and dynamic simulation environment.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. METHODOLOGY","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research Design and Modelling Framework\u003c/h2\u003e \u003cp\u003eThis study adopts a simulation-based, BIM-enabled analytical research design to evaluate the integrated energy, solar, and thermal performance of a residential building. The methodological approach is grounded in the principle that building performance is a multi-domain phenomenon, where energy demand, renewable energy generation, and indoor environmental conditions are intrinsically interdependent. Consequently, rather than applying isolated simulation techniques, this research develops a unified framework that integrates these domains within a single computational workflow. The framework is structured around three core components: (i) BIM-based geometric and material data extraction, (ii) dynamic thermal and energy simulation, and (iii) solar irradiation and photovoltaic (PV) performance analysis. The BIM model serves as the central data repository, enabling consistent transfer of building geometry, construction properties, and spatial zoning into the simulation environment. This ensures that the analysis reflects the actual design configuration rather than simplified abstractions. The simulation workflow follows a sequential yet interconnected process, beginning with model development and parameter definition, followed by system integration within the simulation engine, and culminating in performance evaluation across energy consumption, thermal comfort, and renewable energy generation, as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This integrated design enables the capture of cross-domain interactions, particularly the influence of solar radiation on both PV output and indoor thermal behaviour.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Case Study Description and BIM Model Development\u003c/h2\u003e \u003cp\u003eThe case study comprises a residential apartment located in Guisborough, United Kingdom, selected to represent a typical small-scale housing development within a temperate climatic context. The building consists of a two-level apartment with a total gross floor area of approximately 389.5 m\u0026sup2; and a south-orientated primary fa\u0026ccedil;ade, allowing for meaningful evaluation of solar exposure and passive heating potential. The initial architectural model was developed in Autodesk Revit, which was used for the initial architectural model, while SketchUp was used to refine the geometry for surface consistency, zoning clarity, fenestration definition, and simulation compatibility before transferring the model into the simulation environment, as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The building was subdivided into six thermal zones, each representing distinct functional spaces within the apartment, enabling detailed analysis of spatial variations in thermal behaviour. Construction assemblies were modelled using layered material definitions to accurately represent thermal transmittance properties. The external wall system, for instance, consists of multiple layers, achieving a U-value of 0.240 W/m\u0026sup2;K, while double-glazed window systems with a U-value of 1.1 W/m\u0026sup2;K and solar transmittance of 0.62 were incorporated to capture both insulation performance and solar heat gain characteristics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe building was divided into six thermal zones (A\u0026ndash;F) to capture spatial variations in occupancy, solar exposure, and internal heat gains, as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. This zoning strategy enables a more accurate representation of thermal behaviour compared to single-zone models.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarises the distribution of building area and gross floor area across the defined six (6) thermal zones. Zone F contains by far the largest share of space, with a building area of 201.71 m\u0026sup2; and a gross floor area of 121.35 m\u0026sup2;, making it the dominant zone in the overall layout. Zones E and B also account for substantial portions, recording building areas of 85.79 m\u0026sup2; and 79.87 m\u0026sup2;, respectively, with gross floor areas of 77.15 m\u0026sup2; and 79.97 m\u0026sup2;. In contrast, Zone C has the smallest allocation, with only 27.62 m\u0026sup2; of building area and 15.25 m\u0026sup2; of gross floor area. The total building area across all zones is 501.10 m\u0026sup2;, while the total gross floor area is 389.48 m\u0026sup2;. This indicates that the larger proportion of the building mass and usable floor space is concentrated mainly in Zones F, E, and B, whereas Zones A, C, and D contribute comparatively smaller shares.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of building area and gross floor area across thermal zones.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZone\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilding area (m\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGross floor area (m\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e201.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e121.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTOTAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e501.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e389.48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe building envelope was defined based on typical UK residential construction practices, incorporating multi-layered wall, roof, floor, and ceiling assemblies with specified material properties and thicknesses. These constructions were modelled to accurately represent heat transfer through the building fabric in the simulation environment. The building uses layered construction elements with substantial insulation across the envelope. As seen in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the floor is the thickest element at 450 mm, followed by the external wall at 367.5 mm and the roof at 352.5 mm, showing that thermal protection is concentrated mainly in the ground, wall, and roof assemblies. The ceiling is moderately thick at 247.5 mm, while the internal load wall is the thinnest at 125 mm and is primarily for structural division rather than insulation. The material build-ups combine plaster, masonry, concrete, timber, and insulation to provide structural strength and improved thermal performance, with mineral wool and rigid insulation playing a key role in reducing heat transfer through the building envelope.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConstruction details of building envelope elements, including material composition and layer\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThickness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaterials (outside\u0026ndash;\u0026gt;inside)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExternal wall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e367.5 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlaster 12.5 mm\u0026thinsp;+\u0026thinsp;Brick 102.5 mm\u0026thinsp;+\u0026thinsp;insulated cavity mineral wool 140 mm\u0026thinsp;+\u0026thinsp;concrete block 100 mm\u0026thinsp;+\u0026thinsp;plaster 12.5 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternal load wall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlaster 12.5 mm\u0026thinsp;+\u0026thinsp;concrete block (100 mm) + plaster 12.5 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCeiling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247.5 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlaster \u0026ndash; 12.5 mm\u0026thinsp;+\u0026thinsp;timber joists \u0026ndash; 100 mm\u0026thinsp;+\u0026thinsp;Mineral wool insulation above joists \u0026ndash; 135 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoof\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e352.5 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRoof tiles (clay or concrete) \u0026ndash; 15 mm\u0026thinsp;+\u0026thinsp;Timber battens \u0026ndash; 25 mm\u0026thinsp;+\u0026thinsp;Rafters \u0026ndash; 150 mm\u0026thinsp;+\u0026thinsp;Insulation (mineral wool or PIR) \u0026ndash; 150\u0026ndash;mm + Plaster 12.5 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFloor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e450 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSand 25 mm\u0026thinsp;+\u0026thinsp;Compacted hardcore (Crushed stone) 150 mm\u0026thinsp;+\u0026thinsp;Reinforced concrete slab 100 mm+ Rigid insulation (PIR/EPS) 100 mm\u0026thinsp;+\u0026thinsp;Cement screed 65 mm\u0026thinsp;+\u0026thinsp;Floor finish (tiles/wood/carpet) 10 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Simulation Environment and Model Implementation\u003c/h2\u003e \u003cp\u003eThe building model was implemented in TRNSYS 18, a dynamic simulation platform widely used for transient energy system analysis. The simulation environment enables the integration of multiple system components, including thermal zones, weather data processing, ground temperature modelling, and renewable energy systems within a unified computational structure. The multi-zone thermal simulation was conducted using Type 56, which models heat transfer processes within and between zones based on energy balance equations. Weather data for the simulation was derived from Meteonorm, ensuring location-specific climatic inputs, including solar radiation, ambient temperature, and humidity profiles. Ground temperature effects were incorporated using Type 77, while photovoltaic system performance was simulated using Type 103 components. The simulation was executed with an hourly time step over a full annual cycle (8,760 hours), allowing for high-resolution temporal analysis of building performance. Initial indoor conditions were defined to reflect standard comfort assumptions, ensuring consistency across simulation runs.\u003c/p\u003e \u003cp\u003eThe simulation scheme implemented in TRNSYS is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The central component is Type 56, which performs the multi-zone thermal simulation of the apartment. This component is supported by Type 15, which reads the TMY weather file, and Type 77, which models the ground temperature. Component Type 103b is used to simulate the two PV arrays installed on the building. In addition, the simulation includes plotters, integrators, and data printers for monitoring, processing, and recording the simulation results.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Operational Parameters and Boundary Conditions\u003c/h2\u003e \u003cp\u003eTo ensure realistic representation of building operation, detailed boundary conditions were defined, including occupancy schedules, internal heat gains, ventilation rates, and HVAC control strategies. Occupancy-driven schedules were applied to lighting, appliances, and system operation, reflecting typical residential usage patterns over a 24-hour cycle. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the daily operational profiles adopted in the simulation, including (a) occupancy and lighting, (b) appliance usage, and (c) HVAC and ventilation control. These schedules govern the temporal distribution of internal gains and system operation, thereby influencing both thermal behaviour and energy demand.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInternal heat gains from occupants, equipment, and lighting were estimated based on standard guidelines and uniformly distributed across thermal zones. Air infiltration and ventilation rates were set at 0.3 h⁻\u0026sup1; and 0.4 h⁻\u0026sup1;, respectively, aligning with typical residential ventilation practices and system operation schedules. Thermal comfort was maintained through a heat pump-based HVAC system, with heating and cooling setpoints defined at 21\u0026deg;C and 25\u0026deg;C, respectively, alongside setback temperatures during unoccupied periods. These control strategies were implemented to reflect energy-efficient operation while maintaining acceptable indoor environmental conditions. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the elements considered as heat gain sources, together with their corresponding total thermal power values. Appendix B provides a room-by-room breakdown of the assumed household appliances, including their electrical consumption and associated heat generation according to ASHRAE Handbook [ASHRAE]. In the model, the specific internal gains (per unit floor area) are assumed to be uniformly distributed across all thermal zones\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInternal heat gain assumptions for the building model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeat gain (kJ/h\u0026middot;m\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 persons\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAppliances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTypical appliances for a single-family dwelling in the UK\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLights\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLED lamps\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal, specific internal heat gain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCombined internal heat gain from occupancy, appliances, and lighting\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross Floor Area (m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e389.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal, Floor area of the building model\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Building Envelope and Photovoltaic System Modelling\u003c/h2\u003e \u003cp\u003eThe renewable energy component of the study focuses on the integration of rooftop PV systems within the building model. Two PV arrays were installed on available roof surfaces, with a total of 33 modules distributed across different roof orientations and inclinations. The PV modules were modelled based on manufacturer specifications, including efficiency, nominal power output, and collector area. Solar irradiation on roof surfaces was dynamically calculated using weather data inputs, enabling accurate estimation of incident solar energy and resulting electricity generation. The modelling approach accounts for geometric factors such as roof orientation, tilt angle, and available surface area, which directly influence solar energy capture. This integration allows for simultaneous assessment of solar resource availability and its contribution to building energy demand reduction.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKey thermal and optical properties of the modelled external window\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWindow dimensions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77 \u0026times; 1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003em\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eU-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eg-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpacer type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsulated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrame fraction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrame c-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekJ/h\u0026middot;m\u0026sup2;K\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolar absorptance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmissivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternal convective coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekJ/h\u0026middot;m\u0026sup2;K\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExternal convective coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekJ/h\u0026middot;m\u0026sup2;K\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs seen in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the external window used in the simulation was modelled as an insulated glazing system with dimensions of 0.77 m by 1.08 m. The glazing properties were defined with a U-value of 1.1 W/m\u0026sup2;K and a solar heat gain coefficient (g-value) of 0.62, indicating a thermally efficient window with moderate solar transmittance. An insulated spacer was also specified in the glazing assembly to improve thermal performance and reduce heat loss around the glass edges. In addition to the glazing properties, the frame characteristics were included in the model to provide a more realistic representation of window heat transfer. The frame fraction was set at 0.15, meaning that 15% of the total window area was occupied by the frame. The frame thermal parameter (c-value) was defined as 8.17 kJ/h\u0026middot;m\u0026sup2;K, with solar absorptance and emissivity values of 0.6 and 0.9, respectively. Convective heat transfer coefficients were specified as 11 kJ/h\u0026middot;m\u0026sup2;K for the indoor surface and 64 kJ/h\u0026middot;m\u0026sup2;K for the outdoor surface. Together, these parameters enabled the simulation to capture both conductive and solar heat transfer effects through the window system more accurately.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Performance Evaluation Metrics\u003c/h2\u003e \u003cp\u003eThe performance of the building was evaluated using a set of quantitative indicators that capture energy demand, renewable energy generation, thermal comfort, and environmental impact. Energy performance was assessed through annual heating and cooling demand, as well as total electricity consumption from HVAC systems, appliances, and lighting. Photovoltaic performance was evaluated based on annual electricity generation and solar fraction, defined as the proportion of building energy demand met by solar energy. Additionally, carbon emission reductions were estimated using national emission factors, allowing for assessment of environmental benefits associated with renewable energy integration. Thermal performance was analysed through indoor air temperature profiles across all thermal zones, ensuring compliance with defined comfort thresholds. The integration of these metrics provides a comprehensive understanding of building performance across multiple domains, enabling robust evaluation of design effectiveness.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. RESULTS","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003e4.1 Surface Solar Irradiation Profile\u003c/h2\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e shows the hourly incident solar radiation on the south-facing wall, north-facing wall, and 8\u0026deg; sloped roof over the 8,760-hour simulation period. Under the adopted simulation conditions, solar exposure is clearly non-uniform across the building envelope, confirming that radiation intensity is strongly controlled by orientation and surface inclination. This result is significant because it demonstrates that the building does not operate under a homogeneous irradiance field; instead, different envelope elements are subjected to markedly different solar regimes, with direct consequences for both passive heat gains and active solar energy generation. The sloped roof consistently records the highest solar exposure throughout the year, with substantially greater and more sustained peaks than either of the wall surfaces. This confirms that the roof is the dominant solar collection surface and therefore the most effective location for photovoltaic deployment. Its superior performance is especially evident during spring and summer, when higher solar altitude and longer daylight duration amplify incident radiation. In analytical terms, this establishes the roof as the principal driver of the building\u0026rsquo;s active solar energy potential.\u003c/p\u003e\n\u003cp\u003eThe wall surfaces, by contrast, exhibit lower and more orientation-dependent exposure. The north-facing wall remains the least irradiated surface across almost the entire year, indicating minimal direct solar contribution and limited relevance for useful passive gains. The south-facing wall performs more favourably, particularly during winter, when it provides stronger solar gains than the north fa\u0026ccedil;ade. This is a critical result because it suggests that the south-facing fa\u0026ccedil;ade has greater passive thermal relevance during periods of peak heating demand. Although it does not rival the roof in annual solar capture, its wintertime performance indicates a more strategic role in supporting passive solar heating. This distinction between roof-dominated annual capture and south-fa\u0026ccedil;ade winter responsiveness is important for interpreting the integrated building performance. The roof primarily supports active solar conversion through PV electricity generation, whereas the south wall contributes more selectively to seasonal thermal gains. The solar profile therefore, reveals that different building surfaces support different energy functions, rather than contributing equally to the building\u0026rsquo;s overall performance.\u003c/p\u003e\n\u003cp\u003eThe annual irradiation values for the roof surfaces of Zones A and F are both approximately 991 kWh/m\u0026sup2;, indicating that the two PV locations receive almost identical solar resources. This is analytically important because it rules out solar availability as the main cause of later differences in PV output. Instead, the subsequent variation in electricity generation must be attributed primarily to system-related factors, particularly installed module capacity and collector area. The results demonstrate that solar exposure across the building envelope is both surface-specific and seasonally differentiated. This non-uniformity is not merely descriptive; it underpins the building\u0026rsquo;s later thermal and photovoltaic behaviour and confirms the importance of evaluating solar incidence as a distributed performance variable rather than a single building-wide average.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003e4.2 Indoor Thermal Conditions Across Thermal Zones\u003c/h2\u003e\n\u003cp\u003eIndoor temperatures remained within a comparatively narrow range throughout the annual cycle, despite substantial seasonal variation in external climatic conditions. As illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, the building interior did not passively track outdoor temperature fluctuations; instead, the thermal behaviour of the six zones remained closely regulated around the prescribed comfort thresholds. This indicates that the combined effect of the building envelope, internal gains, and HVAC control strategy was sufficient to dampen the large external temperature swings and maintain stable indoor conditions across the year.\u003c/p\u003e\n\u003cp\u003eThe contrast between external and internal temperature behaviour is particularly pronounced. Outdoor air temperature exhibited wide annual variation, dropping below 0\u0026deg;C during colder periods and rising to approximately 25\u0026deg;C in summer, whereas the indoor zones remained much more stable. Ground temperature followed a smoother and more attenuated seasonal pattern, yet it still varied more than the internal zone temperatures. As shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e, this strong thermal damping confirms that the apartment functioned as a thermally moderated system, where the internal environment was buffered against short-term and seasonal outdoor variability.\u003c/p\u003e\n\u003cp\u003eHowever, thermal stability at whole-building level did not imply complete uniformity across the six zones. Distinct spatial differences remained evident, particularly during the warmer months. Zone A consistently recorded the highest temperatures and showed the strongest summer elevation, while Zone C remained comparatively cooler throughout much of the year. This pattern suggests that local thermal conditions were influenced by zone-specific factors such as solar exposure, glazing, internal heat gains, and spatial configuration. The result is therefore not simply one of stable indoor temperature, but one of controlled yet differentiated thermal response within the same dwelling. This intra-building variation becomes clearer when examined through the monthly averages presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Although indoor temperatures were generally maintained within a comfort-oriented range of approximately 20.0\u0026deg;C to 24.3\u0026deg;C, the spread between zones remained noticeable. Zone A reached a monthly average of 24.3\u0026deg;C in July, whereas Zone C recorded 21.7\u0026deg;C in the same month, giving a difference of 2.6\u0026deg;C. This is analytically significant because it demonstrates that identical control settings did not produce identical thermal outcomes across spaces. Instead, the thermal response remained spatially uneven, even under an overall effective control regime.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMonthly average indoor air temperatures for the 6 thermal zones\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMonth\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eAir Zone Temperature [\u0026ordm;C]\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTA\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTB\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTC\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTD\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTE\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTF\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAvg\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eJan\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFeb\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMar\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eApr\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMay\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eJun\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eJul\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAug\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSep\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOct\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNov\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDec\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAvg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e21.0\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e also shows that this pattern was not limited to a single month. Across the annual cycle, Zone A remained consistently warmer than the other zones, while Zones C and D generally occupied the lower end of the thermal range. The remaining zones fell between these extremes, indicating a gradation of indoor conditions rather than a uniform building-wide profile. This reinforces the evidence from Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e that the apartment achieved overall thermal control, but not complete thermal homogeneity. Taken together, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e indicate that the building achieved two important outcomes simultaneously: seasonal thermal stability and zone-level thermal differentiation. The first confirms the effectiveness of the HVAC and envelope system in maintaining indoor comfort across changing external conditions, while the second reveals the continued influence of spatial and design-related factors on local thermal behaviour. This is a critical finding because it highlights the value of multi-zone analysis: a simplified single-zone model would likely have captured the general stability of the building, but it would have obscured the internal thermal contrasts that are clearly evident in both the hourly and monthly results.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003ch2\u003e4.3 Space-Conditioning Demand\u003c/h2\u003e\n\u003cp\u003ehe space-conditioning results reveal a markedly unbalanced thermal demand profile in which heating overwhelmingly dominates annual building operation, while cooling remains limited in both magnitude and duration. As shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, the building requires heating across a substantial portion of the year, whereas cooling is confined to relatively short periods associated with warmer seasonal conditions. This indicates that the thermal performance of the apartment is fundamentally governed by winter heat losses and seasonal heating requirements rather than by persistent summertime overheating. The dominance of heating is evident not only in annual totals but also in the temporal structure of the load profile. Heating demand extends across most of the simulation year, with sustained peaks during the colder months and only a gradual decline toward summer. Even during transition periods, heating remains present, although at reduced intensity, indicating that the building continues to depend on active thermal input outside the core winter season. By contrast, cooling demand appears only intermittently and is concentrated within a narrow summer window. Its occurrence is sporadic rather than continuous, suggesting that internal temperatures exceed the cooling threshold only under specific short-term combinations of external temperature, solar gains, and internal loads.\u003c/p\u003e\n\u003cp\u003eThis asymmetry is confirmed quantitatively by the annual demand totals. Heating demand reaches 33.2 MWh, whereas annual cooling demand is only 0.36 MWh. The scale of this difference is analytically significant because it shows that cooling represents only a negligible fraction of the total space-conditioning requirement. In practical terms, the building operates as a heating-led system, with cooling contributing very little to annual energy use. The results therefore suggest that the building\u0026rsquo;s climatic and operational context is far more strongly shaped by the need to offset heat loss than by the need to manage prolonged internal overheating. The pattern observed in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e also indicates that the imbalance between heating and cooling is not simply one of annual totals but of annual persistence. Heating is not only larger in magnitude; it is distributed over a much longer part of the year. This prolonged duration points to a building that experiences sustained seasonal demand for thermal support, even if indoor temperatures remain generally stable under the adopted control strategy. Cooling, in contrast, is both low-intensity and short-lived, implying that any overheating tendency is localised and episodic rather than systemic.\u003c/p\u003e\n\u003cp\u003eThis result is important when interpreted alongside the indoor temperature patterns discussed earlier. The fact that the building maintains relatively stable indoor conditions while still recording high annual heating demand indicates that thermal comfort is being achieved through continued active intervention rather than through passive thermal balance alone. At the same time, the very limited cooling requirement suggests that the building envelope and operational controls are broadly effective in preventing excessive summer heat accumulation at whole-building level, even though some zones show relatively higher temperatures during warmer months. The space-conditioning profile confirms that the apartment is strongly heating-dominated, with annual performance shaped primarily by cold-season energy demand. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e and the corresponding annual totals therefore establish a critical feature of the building\u0026rsquo;s operational behaviour: maintaining indoor comfort in this case depends far more on sustained heating provision than on cooling control. This has direct implications for subsequent interpretation of electricity demand, HVAC contribution, and the extent to which photovoltaic generation can offset overall operational energy use.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003ch2\u003e4.4 Photovoltaic Electricity Generation\u003c/h2\u003e\n\u003cp\u003ePhotovoltaic generation followed a strongly seasonal pattern, with output rising progressively from winter into spring, peaking during the high-irradiance months, and then declining again toward the end of the year. As reflected in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e, both roof-mounted arrays responded to the same broad annual solar cycle, but their electrical yields differed substantially in magnitude. This indicates that while seasonal irradiance governed the timing of PV production, system configuration determined the scale of electricity output. The annual pattern shows that PV generation was highly concentrated in spring and summer, when longer daylight hours and stronger solar irradiance increased electricity production. During winter, output from both arrays remained comparatively low, confirming that on-site solar generation made only a limited contribution during the same period when heating demand was highest. This seasonal mismatch is analytically important because it shows that the PV system did not provide a uniform contribution to building energy supply across the year; rather, its effectiveness was strongly dependent on the seasonal availability of solar resources.\u003c/p\u003e\n\u003cp\u003eA clear performance imbalance was observed between the two arrays. The larger roof-mounted system consistently outperformed the smaller one across the entire simulation period, with no period in which their outputs converged meaningfully. Annual generation from the larger array reached approximately 8.0 MWh, whereas the smaller array produced about 1.7 MWh, giving a combined annual PV output of roughly 9.7 MWh. The difference is therefore not marginal or episodic, but structurally embedded in the system configuration.\u003c/p\u003e\n\u003cp\u003eThis distinction is critical in analytical terms because the two roof surfaces were shown earlier to receive almost identical annual irradiation, at approximately 991 kWh/m\u0026sup2;. The difference in electricity generation cannot therefore be attributed primarily to unequal solar resource availability. Instead, the evidence indicates that the gap in output was mainly capacity-driven, resulting from differences in installed collector area and module number rather than locational solar advantage. In practical terms, the higher-yielding array benefited from a much larger deployment scale, accommodating 27 modules compared with only 6 on the smaller array. The dominance of the larger array is therefore a function of system sizing rather than superior exposure conditions. This result has important implications for interpreting the PV contribution within the integrated energy framework. It shows that roof suitability alone does not determine electricity yield; the extent of usable area and the scale of module installation are equally decisive. Thus, although both roof sections were comparably favourable in solar resource terms, their contribution to building electricity supply was highly uneven because one was able to host substantially greater generating capacity.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n\u003ch2\u003e4.5 Monthly Energy Balance and End-Use Distribution\u003c/h2\u003e\n\u003cp\u003eThe monthly energy balance reveals a pronounced seasonal restructuring of the building\u0026rsquo;s energy system, driven by the inverse relationship between space-heating demand and photovoltaic generation. When the main operational components are considered together, as reported in Table\u0026nbsp;6, three patterns become evident: first, heating demand dominates the annual thermal profile; second, appliance and lighting loads remain comparatively stable throughout the year; and third, PV generation is highly seasonal and concentrated in the high-irradiance months. The significance of this integrated view is that it shows not only how much energy is consumed or produced, but also how the timing of demand and on-site generation shapes the building\u0026rsquo;s overall performance.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;6. Monthly energy demand, electricity consumption, photovoltaic production, and efficiency-related outputs.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ekWh_th\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ekWh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ekWh/m2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003ekWh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ekWh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTons\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMonth\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eQ_heat\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eQ_cool\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEle_app\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEle_light\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eH_roof_8\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eH_roof_5\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePV_roof_8\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePV_roof_5\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePV_total\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCOP HVAC\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHVAC_pow\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eConsump Elect\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSolar F [-]\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003etCO2 saved\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eJan\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5742.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e812.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e168.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e151.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e186.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1914.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2895.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFeb\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5008.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e733.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e147.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e281.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e345.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1669.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2550.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMar\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4346.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e812.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e161.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e76.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e611.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e140.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e752.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1448.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2422.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eApr\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2934.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e786.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e151.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e112.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e114.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e946.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e206.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1153.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e978.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1915.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMay\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1565.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e812.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e152.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e155.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e154.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1265.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e282.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1548.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e524.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1488.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eJun\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e605.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e786.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e145.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e148.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e150.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1219.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e266.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1485.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e211.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1142.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.33\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eJul\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e163.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e188.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e812.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e150.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e150.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e151.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1212.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e266.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1479.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e117.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1079.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.33\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAug\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e202.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e101.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e812.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e158.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd 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align=\"left\"\u003e\n\u003cp\u003e451.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e783.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1758.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNov\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4109.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e786.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e161.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e184.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e227.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1369.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2317.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDec\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5573.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e812.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e168.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e115.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e140.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1857.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2838.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAvg\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2771.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e797.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e157.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e666.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e148.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e814.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e933.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1887.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.18\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTOTAL\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e33257.2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e359.2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e9563.9\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1883.4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e991.6\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e991.0\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e8000.1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1778.7\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e9778.8\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e11205.5\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e22652.8\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e2.2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eThe thermal demand profile remains strongly winter-oriented. Monthly heating demand is highest in January, December, and February, at 5742.3 kWh, 5573.0 kWh, and 5008.9 kWh respectively, before falling sharply toward summer. Cooling demand, by contrast, remains negligible for most of the year and becomes noticeable only during the warmest months, peaking at 188.1 kWh in July. This reinforces the earlier result that the building is fundamentally heating-led rather than cooling-led. However, the value of Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e lies in showing how this thermal behaviour translates into electricity demand through HVAC operation. With an average heat pump COP of 3.0, annual HVAC electricity consumption reaches 11,205.5 kWh, confirming that space conditioning constitutes the single largest electricity end use in the apartment.\u003c/p\u003e\n\u003cp\u003eAt the same time, non-HVAC electricity demand remains relatively stable across the year. Annual electricity use from appliances is 9563.9 kWh, while lighting accounts for 1883.4 kWh. Unlike heating demand, these loads show only modest monthly variation, which means that the building retains a consistent base electricity demand even when seasonal thermal loads decline. This stability is analytically important because it prevents summer electricity demand from falling in direct proportion to the reduction in heating load. In other words, the building\u0026rsquo;s annual electricity profile is shaped by the interaction between a variable HVAC load and a persistent plug-and-lighting base load.\u003c/p\u003e\n\u003cp\u003eThis interaction becomes more meaningful when compared with PV production. As shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and synthesised in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e, PV generation increases from 186.5 kWh in January to a peak of 1548.3 kWh in May, remaining high through June and July before declining again into autumn and winter. The pattern is therefore highly seasonal and closely tied to irradiance availability. Annual PV production totals 9778.8 kWh, of which 8000.1 kWh is generated by the larger roof array and 1778.7 kWh by the smaller one. The key analytical point is that PV generation peaks precisely when heating-related electricity demand is at its lowest, producing a seasonal reversal in the balance between demand and on-site supply.\u003c/p\u003e\n\u003cp\u003eThis mismatch between seasonal demand and seasonal production is one of the most important results in the study. During winter, electricity consumption remains high because of sustained heating requirements, while PV output is minimal. In January, for example, total electricity consumption is 2895.1 kWh, compared with PV generation of only 186.5 kWh, giving a solar fraction of just 0.06. A similar pattern is observed in December and November, where solar fractions remain at 0.05 and 0.10, respectively. These figures indicate that the contribution of on-site solar generation to building operation is very limited during the months when energy demand is greatest. By contrast, the balance shifts substantially from late spring into summer. In April, the solar fraction rises to 0.60, indicating that PV generation covers 60% of electricity demand. From May onward, the relationship becomes even more pronounced: the solar fraction reaches 1.04 in May, 1.30 in June, 1.37 in July, and 1.09 in August. This means that during these months, on-site PV production is sufficient not only to offset the building\u0026rsquo;s electricity demand but also to exceed it. The transition from winter deficit to summer surplus, visible in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e, demonstrates that the building does not experience a constant renewable contribution across the year; instead, it moves between two distinctly different operating states depending on season. This seasonal imbalance has direct implications for how the PV system should be interpreted. On an annual basis, on-site generation is substantial, but its usefulness is temporally uneven. The system performs strongly during months of low heating demand and weakly during months of high heating demand. As a result, annual totals alone risk overstating the functional alignment between renewable generation and building energy need. The monthly balance reported in Table\u0026nbsp;6 is therefore more informative than annual aggregation, because it reveals that the building\u0026rsquo;s apparent renewable performance depends heavily on when energy is required, not only on how much is produced over the year\u003c/p\u003e\n\u003cp\u003eThe end-use distribution further clarifies the structure of total annual electricity consumption. As summarised in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e, HVAC accounts for approximately 49.5% of annual electricity demand, appliances contribute 42.2%, and lighting represents 8.3%. This disaggregation is analytically useful because it shows that although heating dominates the seasonal profile, the building\u0026rsquo;s annual electricity demand is not exclusively driven by HVAC. Appliances alone account for a very substantial share of consumption, almost approaching the HVAC contribution. This means that even if space-heating demand is reduced, a large non-thermal electricity load would remain. Lighting, by contrast, makes the smallest contribution and has relatively limited influence on the annual demand structure. Taken together, Table\u0026nbsp;6, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e, and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e show that the building\u0026rsquo;s energy behaviour is defined by a dynamic interaction between a highly seasonal heating load, a comparatively stable appliance-and-lighting base load, and a PV system whose output is concentrated in the high-irradiance months. The resulting pattern is neither balanced nor constant across the year. Instead, it is characterised by winter dependence on imported electricity and summer periods of strong solar offset and seasonal surplus. This integrated energy picture is critical because it moves beyond isolated reporting of demand or generation and shows how the building actually performs as a coupled thermal-electrical system over time.\u003c/p\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n\u003ch2\u003e4.6 Carbon Emissions Savings\u003c/h2\u003e\n\u003cp\u003eThe carbon performance of the building was assessed by estimating the emissions avoided through the use of on-site photovoltaic electricity in place of grid-supplied electricity. The avoided emissions were calculated from the share of total monthly electricity demand met by PV generation, using the solar fraction as the key conversion term. As expressed in Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), the solar fraction was multiplied by the total monthly electricity consumption and the baseline grid emission factor for the UK electricity sector.\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ1\" class=\"mathdisplay\"\u003e$$\\:{\\text{CO}}_{2}{\\text{ Saved (tCO}}_{2}\\text{)}=\\text{Solar fraction}\\times \\text{Total Elect (MWh)}\\times {\\text{Emission factor (tCO}}_{2}\\text{/MWh)}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eA weighted average baseline emission factor of 0.22535 tCO₂/MWh was adopted for the UK power sector. On this basis, the avoided emissions profile closely follows the seasonal behaviour of photovoltaic generation rather than the overall electricity demand profile. This is analytically important because it shows that carbon savings are governed primarily by the timing of renewable electricity availability, not simply by the magnitude of annual consumption. The results indicate that avoided emissions remain low during winter, when solar generation contributes only a small fraction of total electricity demand. Monthly savings are 0.04 tCO₂ in January, 0.08 tCO₂ in February, 0.05 tCO₂ in November, and 0.03 tCO₂ in December, reflecting the weak contribution of PV during the same period in which heating-related electricity demand is highest. This confirms that the carbon reduction effect of the PV system is limited at the point in the year when grid dependence is greatest. By contrast, avoided emissions increase sharply from spring into summer as solar generation rises and the solar fraction improves. Monthly savings reach 0.17 tCO₂ in March, 0.26 tCO₂ in April, and 0.35 tCO₂ in May, before remaining high at 0.33 tCO₂ in both June and July. Although electricity demand is lower during these months, the much stronger PV contribution produces the greatest carbon benefit. This pattern demonstrates that the emissions reduction potential of the system is concentrated in the high-irradiance period rather than being evenly distributed across the annual cycle. This seasonal concentration is critical to the interpretation of the building\u0026rsquo;s environmental performance. On an annual basis, the PV system avoids approximately 2.2 tCO₂, confirming that on-site solar generation contributes a measurable reduction in operational carbon emissions. However, the monthly pattern shows that this benefit is temporally uneven and closely tied to the same winter-demand versus summer-generation mismatch observed in the energy balance analysis. In effect, the system delivers its strongest carbon savings when solar availability is highest, rather than when the building\u0026rsquo;s electricity dependence is greatest. This means that the annual carbon benefit is meaningful, but its practical effectiveness is moderated by the limited coincidence between peak renewable generation and peak energy demand. The carbon emissions results confirm that the photovoltaic system improves the environmental performance of the building, but they also expose the seasonal limitation of this benefit. The avoided emissions are substantial in annual terms, yet they are produced under conditions of uneven monthly alignment between renewable supply and building demand. This reinforces the broader result that annual sustainability gains should not be interpreted only in aggregate terms, since the temporal distribution of those gains is equally important in understanding actual building performance.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n\u003ch2\u003e4.7 Synthesis of Key Quantitative Results\u003c/h2\u003e\n\u003cp\u003eTaken together, the results define a building with a strongly heating-dominated but thermally stable annual performance profile. Solar exposure was highest on the sloped roof, with both roof sections receiving approximately 991 kWh/m\u0026sup2; annually, confirming their suitability for PV deployment, while the south-facing wall showed stronger winter solar gain than the north-facing wall. Indoor temperatures across the six zones remained within a controlled range of 20.0\u0026ndash;24.3\u0026deg;C, although zone-level differences persisted, with Zone A consistently recording the highest temperatures and Zone C the lowest. Space-conditioning demand was overwhelmingly driven by heating, which reached 33.2 MWh annually, compared with only 0.36 MWh for cooling. This translated into an annual HVAC electricity use of 11.2 MWh, making HVAC the largest single electricity end use. Total annual electricity consumption was 22.6 MWh, comprising 11.2 MWh for HVAC, 9.6 MWh for appliances, and 1.8 MWh for lighting. The PV system generated 9.7 MWh annually, with the larger array contributing 8.0 MWh and the smaller array 1.7 MWh. Although both arrays received near-identical solar resources, the larger system dominated output because of its greater installed capacity. The seasonal energy balance showed low winter solar fractions of 0.05\u0026ndash;0.14, rising to 0.60 in April and exceeding 1.0 between May and August, indicating seasonal electricity surplus during the high-irradiance period. This renewable contribution produced annual avoided carbon emissions of approximately 2.2 tCO₂. Monthly savings were lowest in winter and highest in late spring and summer, reaching about 0.35 tCO₂ in May and 0.33 tCO₂ in both June and July. Overall, the results show that the building achieves stable indoor comfort and meaningful renewable energy contribution, but that its operational performance remains strongly shaped by winter heating demand and the seasonal mismatch between peak demand and peak solar generation.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. DISCUSSION","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Heating-Dominated Energy Profile and Climatic Context\u003c/h2\u003e \u003cp\u003eThe findings indicate that the building functions predominantly in a heating-dominated environment, exhibiting an annual heating demand of 33.2 MWh, contrasted with a mere 0.36 MWh for cooling. This significant disparity suggests that the primary operational problem in this climatic environment is not summer overheating, but the continuous necessity to counteract heat loss over an extended duration of the year. The building's energy performance is predominantly influenced by winter thermal vulnerability rather than cooling demand. This trend aligns with the climatic circumstances in Guisborough and, more generally, with residential structures in temperate countries, where moderate summer weather and prolonged cold seasons usually necessitate significantly higher heating than cooling demands (Reinhart \u0026amp; Cerezo Davila, 2016). The importance of the current result is not solely in affirming this general trend, but in demonstrating its robust persistence even within a structure that upholds generally steady indoor temperatures. The little cooling demand indicates that the envelope and management technique were generally successful in mitigating excessive summer heat accumulation at the whole-building level, albeit some localised warming in areas with increased sun exposure. The minimal cooling demand is especially indicative when analysed in conjunction with the indoor temperature outcomes. Despite the observation of elevated temperatures in Zone A throughout the summer months, these localised increases did not result in a significant annual cooling demand. This indicates that solar gains via exposed fa\u0026ccedil;ades and glazing influenced regional variations in indoor temperature without fundamentally transitioning the structure into a cooling-dominant or mixed-demand regime. The implication is that passive solar impacts in this instance are more significant for local thermal variation than for annual cooling energy consumption. This outcome has two significant implications regarding performance. Initially, it suggests that energy efficiency measures in similar temperate-climate residential structures are expected to produce more significant advantages when focused on decreasing heating demand via enhanced insulation, airtightness, and control optimisation, rather than emphasising cooling reduction. Secondly, it indicates that annual building performance in these situations cannot be only assessed by total power consumption, as the primary factor influencing seasonal energy fluctuations is the demand for thermal cooling during winter operations. The result methodologically emphasises the significance of dynamic simulation. The great seasonal concentration of heating demand and the very limited, short-duration nature of cooling would be difficult to explain appropriately using simplistic steady-state techniques. The simulation accurately reflects the building's performance under real seasonal conditions by capturing hourly variations in climatic response, internal gains, and control behaviour. The findings consequently justify the continued use of comprehensive dynamic modelling methodologies, such as TRNSYS, for measuring climate-responsive residential energy performance (Crawley et al., 2001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Indoor Thermal Stability and Spatial Variation\u003c/h2\u003e \u003cp\u003eThe indoor thermal findings demonstrate that the structure attained a significant level of seasonal stability, with zone temperatures consistently held between 20.0\u0026ndash;24.3\u0026deg;C despite considerable fluctuations in external conditions. This verifies that the synergistic effect of the envelope, internal gains, and HVAC management method successfully mitigated external climatic variations and sustained acceptable inside conditions year-round. The importance of this discovery resides not merely in the stability, but in its coexistence with distinct geographical diversity among zones.\u003c/p\u003e \u003cp\u003eThis finding aligns with other research indicating that well regulated HVAC systems can sustain constant indoor thermal conditions amidst fluctuating meteorological circumstances (Hensen and Lamberts, 2011). However, the current findings introduce a significant nuance: thermal regulation at the whole-building scale did not achieve thermal uniformity at the zone level. Zone A generally maintained higher temperatures than the other areas, especially throughout summer, whilst colder zones like Zone C displayed a more temperate reaction. This indicates that local elements, such as solar exposure, glazing attributes, and spatial arrangement, persistently influenced indoor conditions despite a uniform management system.\u003c/p\u003e \u003cp\u003eThe results correspond more closely with research that highlight the geographical variety of interior thermal behaviour. Allegrini et al. (2015) shown that solar exposure and local boundary conditions can markedly affect zone-level thermal response, even when buildings are assessed under a same operational framework. Likewise, extensive research on building performance has consistently demonstrated that single-zone models often diminish local temperature variations, perhaps exaggerating the homogeneity of indoor comfort levels. The current data substantiate that critique by revealing a quantifiable intra-building temperature disparity, especially during warmer months, despite the building's overall stability when assessed in aggregate terms. This comparison is analytically significant as it demonstrates that consistent average indoor circumstances should not be construed as proof of uniform thermal performance. The results thus underscore the significance of multi-zone simulation in evaluating home performance. Simplified models may suffice for general annual energy estimates, but they inadequately represent the spatially varied thermal behaviour essential for assessing comfort. The current work demonstrates that passive and active systems do not function separately; instead, their interaction results in a building that is thermally stable yet internally heterogeneous. This provides a more accurate depiction of residential performance and underscores the necessity of assessing indoor comfort not alone through annual averages, but also by considering zone-level variations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Photovoltaic Performance and Seasonal Energy Dynamics\u003c/h2\u003e \u003cp\u003eThe photovoltaic data indicate that annual generation was significant, albeit operationally inconsistent. The system generated a total photovoltaic output of 9.7 MWh, contributing significantly to the building's yearly electricity requirement of 22.6 MWh. Nonetheless, the more analytically significant finding is that this contribution exhibited considerable seasonality: generation peaked in spring and summer, while winter output remained subdued despite the period of highest heating-related demand. This indicates that annual PV yield, by itself, is an inadequate measure of operational efficiency, as it obscures the temporal discrepancy between renewable electricity supply and real building demand.\u003c/p\u003e \u003cp\u003eThis pattern aligns with prior research on PV-supported structures and urban energy systems in temperate regions. Sola et al. (2018) noted that while solar solutions can substantially enhance building energy performance, their efficacy is frequently limited by the seasonal disparity between maximum sun availability and peak thermal demand. Mutani and Todeschi (2020) shown that solar contributions in building and neighbourhood systems are significantly influenced by climatic seasonality, with peak renewable output occurring when space-heating demand is relatively low. The current data substantiate this interpretation: the minimal winter solar percentages and the summer excess from May to August do not signify an exceptional phenomenon of the case-study building, but rather indicate a fundamental constraint of rooftop photovoltaic systems in heating-dominated climates.\u003c/p\u003e \u003cp\u003eThe disparity between the two arrays elucidates the factors influencing PV yield in this instance. The significantly higher output from Array F cannot be attributed mainly to variations in solar resources, as both roof areas saw about comparable yearly irradiation. Instead, the finding verifies the claim by Jakubiec and Reinhart (2013) that PV power generation is determined not just by incident radiation, but also by geometric representation, usable roof area, and deployment arrangement. The predominance of Array F is thus most accurately understood as a capacity impact resulting from the quantity of modules and the collector area, rather than from any significant locational benefit in sun exposure. This distinction is significant, since it transitions the explanation from mere surface exposure to system scale and layout as the principal determinants of yield.\u003c/p\u003e \u003cp\u003eThe current results thus expand upon prior findings by demonstrating the operation of this seasonal mismatch inside a comprehensive BIM-enabled household model. Although the building demonstrates significant renewable energy contribution in summer and even generates a seasonal electrical surplus, it remains reliant on external electricity during winter. This reinforces the prevailing view in the literature that annual energy balance metrics may exaggerate the actual decarbonisation benefits of building-integrated photovoltaics if temporal supply-demand alignment is not taken into account (Sola et al., 2018; Mutani and Todeschi, 2020). The PV system has strong performance in annual aggregate metrics, although is less efficient when assessed in relation to the timing of real energy demand.\u003c/p\u003e \u003cp\u003eThis analysis further substantiates the assertion that photovoltaic integration alone is improbable to address the operational energy challenges of heating-dominant residential structures. Kaviani et al. (2023) assert that the efficacy of on-site renewables is enhanced when integrated with broader system methods, such storage, load shifting, or more adaptive energy management. In the context of this study, rooftop photovoltaic systems markedly enhance annual power performance; nonetheless, they do not eradicate winter grid need, as the building's peak demand coincides with periods of minimal solar generation. The primary result is that photovoltaic (PV) systems are advantageous, but their true efficacy is contingent upon the alignment of their temporal output profile with the seasonal demand patterns of the building.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Integrated Energy Balance and End-Use Distribution\u003c/h2\u003e \u003cp\u003eThe integrated energy results indicate that HVAC constituted the predominant power end use, with about fifty percent of total annual consumption, while the balance was mostly linked to appliances and, to a lesser degree, lighting. This distribution aligns with prior home energy research in temperate settings, where space conditioning generally predominates operating power usage (Reinhart and Cerezo Davila, 2016). Nonetheless, the current findings indicate that non-HVAC loads continued to be sufficiently strong to maintain a significant base demand year-round, despite a reduction in heating demand.\u003c/p\u003e \u003cp\u003eA significant discovery is that the link between demand and on-site generation was highly dynamic rather than static. The contribution of photovoltaic (PV) systems was constrained throughout winter, coinciding with peak electricity consumption, although experienced a significant increase in late spring and summer, resulting in intervals of seasonal surplus. This underscores the assertion by Sola et al. (2018) that annual energy totals alone may obscure significant temporal discrepancies between renewable generation and building demand. This analysis revealed that the structure exhibited varying performance throughout the year, transitioning from a winter deficit to a summer surplus. This underscores the significance of integrated simulation methodologically. This study assesses HVAC demand, end-use power, and PV output within a unified framework, thereby highlighting interactions frequently neglected when energy demand and renewable supply are examined independently. The findings advocate for more integrated performance assessment methodologies, especially for residential structures where seasonal demand-generation discrepancies significantly impact overall energy behaviour.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Carbon Emissions Reduction and Environmental Implications\u003c/h2\u003e \u003cp\u003eThe yearly carbon reduction of roughly 2.2 tCO₂ demonstrates that rooftop photovoltaic systems can achieve a significant decrease in operational emissions in residential structures. This aligns with other research indicating that on-site renewable energy can significantly aid in operational decarbonisation, especially in contexts of considerable electricity demand (Mutani and Todeschi, 2020). In this instance, the importance of the outcome resides more in the irregular distribution of benefits throughout the year than in the annual total itself. The monthly trend indicates that emissions reduction closely aligned with photovoltaic generation, with the most significant savings observed in late spring and summer, and considerably lesser reductions during winter. This substantiates the assertion that annual carbon totals may exaggerate actual environmental advantages if the timing of renewable energy generation is disregarded. According to Sola et al. (2018), the significance of renewable contributions is contingent not only on the quantity of energy generated but also on the timing of such production in relation to demand from the building. The findings indicate that photovoltaic integration enhances environmental performance; nonetheless, it does not independently address the more profound decarbonisation challenge associated with heating-dominated buildings. According to Kaviani et al. (2023), enhanced carbon reduction is likely contingent upon the amalgamation of renewable energy generation, reduced heating demand, increased efficiency, and superior system integration. The consequence is that photovoltaic (PV) technology is a crucial decarbonisation strategy; nevertheless, its efficacy is contingent upon the seasonal disparity between supply and demand.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e5.6 Global Implications for Temperate Climate Regions\u003c/h2\u003e \u003cp\u003eThe implications of this study extend beyond Guisborough to include residential buildings in temperate marine and cool-temperate climates, covering substantial areas of Northern and Western Europe, select regions of North America, and particular sites in East Asia. In these settings, analogous environmental traits\u0026mdash;mild summer conditions, lengthy heating seasons, and seasonally changing solar availability\u0026mdash;typically give building performance patterns akin to those reported in our study. A primary finding is that household decarbonisation in temperate zones will primarily provide a heating problem rather than a cooling one. The considerable dominance of heating demand compared to cooling demand indicates that the most significant efficiency enhancements are expected to result from reducing heat loss through improved envelope design, insulation, airtightness, and control systems, rather than prioritising cooling-oriented solutions. This suggests that, under these circumstances, energy policy and design practice must prioritise heating reduction as the foremost strategy to diminish operating energy usage.\u003c/p\u003e \u003cp\u003eThe findings demonstrate that photovoltaic systems can significantly improve annual electricity generation; nonetheless, their role in temperate climates is mostly supplementary rather than entirely transformative when used in isolation. The photovoltaic system in this study substantially reduced annual electricity usage and generated a seasonal surplus in summer; however, its contribution during winter was negligible. This highlights a broader structural issue in temperate regions: the seasonal misalignment between the availability of renewable electricity and peak building demand. Thus, the effectiveness of photovoltaic deployment is maximised when assessed alongside storage, demand flexibility, and comprehensive grid integration, rather than as a standalone solution. A further notable implication relates to indoor environmental performance. The results demonstrate that stable indoor temperatures may be maintained despite varying external conditions; yet, localised thermal variations persist within the same dwelling. This is directly relevant to housing standards and performance-based regulation, as it suggests that adequate whole-building performance does not necessarily provide uniform comfort across all areas. In dwellings located in temperate climates, it is crucial to simultaneously evaluate energy efficiency and localised comfort.\u003c/p\u003e \u003cp\u003eThe study methodically demonstrates the importance of BIM-enabled integrated simulation for residential assessment in these regions. This method consolidates energy demand, solar potential, and indoor thermal performance into a cohesive framework, providing a more precise basis for decision-making during the design phase than fragmented or overly simplified approaches. This is especially relevant in temperate regions, where housing is often defined by small- to medium-scale developments, and where design-specific features can significantly influence performance outcomes.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. CONCLUSION","content":"\u003cp\u003eThis study developed and applied a BIM-enabled integrated simulation framework for assessing the energy demand, photovoltaic potential, and indoor thermal performance of a residential building using a detailed as-designed model. The study responds to the persistent fragmentation in conventional building performance research, where energy, solar, and thermal analyses are often undertaken separately. By integrating these domains within a unified BIM-to-simulation workflow, the research demonstrates the value of a more holistic and practically reproducible approach to residential building performance assessment.\u003c/p\u003e \u003cp\u003eThe findings show that the case-study building exhibits a strongly heating-dominated annual performance profile. Annual heating demand reached 33.2 MWh, while cooling demand remained minimal at 0.36 MWh, confirming that building operation in this temperate context is governed primarily by winter heat loss rather than summer overheating. Despite this, indoor conditions remained comparatively stable across the six thermal zones, with temperatures generally maintained within a comfort range of about 20.0\u0026ndash;24.3\u0026deg;C throughout the year. This indicates that the building envelope, internal gains, and HVAC control strategy were effective in maintaining overall thermal comfort, although noticeable zone-level differences persisted, particularly during warmer months. The results therefore highlight both the thermal resilience of the building and the importance of multi-zone analysis in revealing internal spatial variation that would be masked in simplified whole-building models.\u003c/p\u003e \u003cp\u003eThe solar and renewable energy results further demonstrate the benefit of integrated assessment. The roof surfaces received the highest incident solar radiation, confirming their suitability for photovoltaic deployment. The 33-module PV system generated approximately 9.7 MWh annually, with the larger array accounting for most of the total output due to its greater installed capacity. Although annual PV generation was substantial, its contribution was highly seasonal. Solar fractions remained low during winter, when heating demand and electricity dependence were highest, but increased significantly from spring to summer, exceeding 1.0 between May and August and indicating seasonal electricity surplus. This confirms that the building achieved meaningful on-site renewable contribution, but also reveals a strong temporal mismatch between peak energy demand and peak solar generation. On an annual basis, the system avoided approximately 2.2 tCO₂ emissions, demonstrating measurable environmental benefit, though this benefit was unevenly distributed across the year.\u003c/p\u003e \u003cp\u003eOverall, the study confirms that a BIM-enabled integrated workflow can provide a realistic and robust basis for evaluating residential building performance across interconnected domains. The originality of the work lies not only in combining energy, solar, and thermal analyses, but in doing so through a fully detailed BIM-derived model without excessive geometric simplification. This enhances methodological transparency, preserves design fidelity, and improves the practical relevance of simulation outputs for early-stage decision-making and performance-informed residential design.\u003c/p\u003e \u003cp\u003eNotwithstanding these contributions, the study has several limitations. First, the analysis is based on a single residential case study, which restricts the generalisability of the findings to other housing typologies, scales, or climatic contexts. Second, the simulation assumes fixed occupancy schedules and system operation patterns, which may not fully reflect the variability of real occupant behaviour. Third, the photovoltaic assessment does not incorporate energy storage or grid interaction strategies, both of which would influence the effective use of surplus summer electricity. In addition, the use of typical meteorological year data means that localised microclimatic effects and short-term weather extremes are not explicitly captured. Finally, although the study adopts a BIM-enabled workflow, the requirement for intermediate model refinement and processing indicates that full interoperability between design and simulation environments remains an unresolved challenge.\u003c/p\u003e \u003cp\u003eFuture research should therefore extend this framework beyond a single building to multiple-building or neighbourhood-scale applications in order to examine collective energy behaviour, distributed photovoltaic generation, and shared renewable infrastructure. Further work should also incorporate battery storage, demand response, and smart grid interactions to provide a more complete picture of operational energy flexibility. In methodological terms, improving BIM-to-simulation interoperability remains an important priority, especially through the development of more standardised and automated data exchange workflows that reduce manual preprocessing. Additional studies may also improve simulation realism by integrating measured occupancy patterns, monitored operational data, and higher-resolution climatic inputs. Such advances would strengthen the applicability of BIM-enabled integrated assessment as a practical tool for the design of low-carbon, energy-efficient, and climate-responsive residential buildings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e-\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e -\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors consent to the publication of this manuscript.\u003c/p\u003e\n\u003ch2\u003eClinical trial number\u003c/h2\u003e\n\u003cp\u003e-Not applicable.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e -\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003ch2\u003eFunding -\u003c/h2\u003e\n\u003cp\u003eThis research did not receive any funding.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eConceptualisation, methodology, formal analysis; Investigation, writing-review and editing- E.D.O, J.U; writing-review and editing, project administration, and Validation- C.K.M; J.U. writing-review and editing, project administration, and Validation, Investigation \u0026ndash; O.A. All authors have read and agreed to publish this manuscript in your journal.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eAll data generated will be made available on reasonable request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAgboola, O. P., Ojobo, H., \u0026amp; Aliyev, A. (2023). Ameliorating climate change impacts on the built environment.Civil Engineering and Architecture, 11(3), 1324\u0026ndash;1336 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.13189/cea.2023.110317\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n \u003cli\u003eAhsan, A. (2024). Integration of BIM and GIS for smart construction management (Master of Science dissertation, Politecnico di Torino). Retrieved 2nd February 2026 from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://webthesis.biblio.polito.it/id/eprint/30399\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eAlexandrou, K., Thravalou, S., \u0026amp; Artopoulos, G. (2024). Heritage-BIM for energy simulation: a data exchange method for improved interoperability. Building Research \u0026amp; Information, 52(3), 373\u0026ndash;386. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09613218.2023.2222856\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003eAli, U., Shamsi, M. H., Hoare, C., Mangina, E., \u0026amp; O\u0026rsquo;Donnell, J. (2019). A data-driven approach for multi-scale building archetypes development. 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Buildings, 13(11), 2868. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/buildings13112868\u003c/span\u003e\u003cspan class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-building-pathology-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpar","sideBox":"Learn more about [Journal of Building Pathology and Rehabilitation](http://link.springer.com/journal/41024)","snPcode":"41024","submissionUrl":"https://submission.nature.com/new-submission/41024/3","title":"Journal of Building Pathology and Rehabilitation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Building Information Modelling (BIM), Building Energy Simulation, Photovoltaic Systems (PV), Thermal Comfort, Residential Buildings, Integrated Simulation Framework, Energy Performance Assessment, Carbon Emissions Reduction","lastPublishedDoi":"10.21203/rs.3.rs-9450013/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9450013/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe increasing demand for low-carbon and energy-efficient residential buildings has intensified the need for integrated performance assessment frameworks that can simultaneously evaluate energy consumption, renewable energy potential, and indoor thermal conditions. Conventional building energy studies often rely on isolated simulation approaches, limiting the ability to capture the interdependencies between building systems, solar resources, and occupant comfort. This gap is particularly evident in small-scale residential developments, where practical, simulation-ready workflows remain underdeveloped. This study proposes a BIM-enabled integrated simulation framework for assessing energy demand, photovoltaic (PV) potential, and thermal performance in residential buildings. A detailed multi-zone building model was developed from an as-designed BIM model and implemented in TRNSYS, incorporating construction properties, occupancy schedules, internal heat gains, and local climatic data for Guisborough, United Kingdom. The framework integrates energy modelling, solar irradiation analysis, and HVAC system simulation within a unified workflow. The results demonstrate that the building maintains stable indoor thermal conditions within comfort ranges (20\u0026ndash;24.3\u0026deg;C) throughout the year while exhibiting a dominant heating demand of 33.2 MWh and minimal cooling requirements. The integration of a 33-module PV system generates approximately 9.7 MWh annually, covering up to 60% of the building\u0026rsquo;s electricity demand and achieving a seasonal energy surplus during summer months. The system contributes to an estimated annual reduction of 2.2 tCO₂ emissions. This study is original in using a fully detailed BIM-derived model without geometric simplification to enable realistic, reproducible multi-domain assessment. Unlike conventional methods, it integrates energy, solar, and thermal analysis within a practical workflow for early-stage design and small-scale residential buildings.\u003c/p\u003e","manuscriptTitle":"Integrated BIM-Based Simulation of Energy, Photovoltaic, and Thermal Performance in Residential Buildings","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-13 13:32:25","doi":"10.21203/rs.3.rs-9450013/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-14T20:15:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34925888986486630825280629236842052072","date":"2026-05-14T19:55:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T22:14:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"290488012151156979116244408690735660452","date":"2026-05-05T12:08:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-05T08:58:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-03T11:35:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-20T02:27:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Building Pathology and Rehabilitation","date":"2026-04-17T13:46:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-building-pathology-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpar","sideBox":"Learn more about [Journal of Building Pathology and Rehabilitation](http://link.springer.com/journal/41024)","snPcode":"41024","submissionUrl":"https://submission.nature.com/new-submission/41024/3","title":"Journal of Building Pathology and Rehabilitation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"aa6135fd-9cf2-4441-9c35-62339866d778","owner":[],"postedDate":"May 13th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-14T20:15:23+00:00","index":20,"fulltext":""},{"type":"reviewerAgreed","content":"34925888986486630825280629236842052072","date":"2026-05-14T19:55:30+00:00","index":19,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T22:14:43+00:00","index":15,"fulltext":""},{"type":"reviewerAgreed","content":"290488012151156979116244408690735660452","date":"2026-05-05T12:08:08+00:00","index":14,"fulltext":""},{"type":"reviewersInvited","content":"9","date":"2026-05-05T08:58:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-03T11:35:02+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T13:32:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-13 13:32:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9450013","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9450013","identity":"rs-9450013","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}