AI Powered Renewable Energy Balancing, Forecasting and Global Trend Analysis using ANN-LSTM Integration

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These instabilities, mainly due to fluctuating weather conditions, may lead to surpluses or shortages of energy-with inevitable effects on the grid's reliability. It is proposed that an AI-enabled system based on ANN and LSTM solutions be developed to analyse global energy trends, predict renewable generation accurately, and enhance the grid's resilience. The new model resides on the historical and real-time energy data and adequately captures the long-range transition of energy and the short-range fluctuations in energy, allowing better energy management. Along with that, the intelligent forecasting will also optimize energy storage and minimize the overreliance on normal fossil fuel energy. The insights drawn out by this model provide considerable assistance to decision-makers, energy suppliers, and grid operators in their drive for a more stable, efficient, dependable, and sustainable energy infrastructure. This research highlights the significant role that AI-driven predictive analytics should play in facilitating global transitions toward renewable energy while addressing some of the critical operational challenges to grid reliability and energy distribution. Artificial Intelligence and Machine Learning Grid Stability Renewable Energy Balancing ANN-LSTM Integration Global Renewable Energy Forecasting Machine Learning in Energy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction The global shift towards renewable energy has become increasingly crucial, motivated by an urgent need to address climate change, exhaust fossil fuel supplies, and satisfy the rising demand for energy worldwide. The use of traditional energy sources such as coal, oil, and natural gas has significantly contributed to escalated carbon emissions and environmental harm. In response to these challenges, governments, industries, and researchers are expediting the transition to cleaner and more sustainable forms of energy like solar, wind, or hydroelectric power (Milani et al., 2020; Renné, 2022; Rout et al., 2025). Nevertheless, despite their vast potential benefits, renewable energy options come with distinct challenges related to their inconsistency and dependence on weather conditions. However, such uncertainties render optimal stability and reliability of the electricity supply very difficult to achieve; solar power generation changes with various weather patterns, while wind energy relies on various degrees of wind speed (Khalilnejad & Riahy, 2014; Salman & Teo, 2003; Zhang & Wan, 2014). The pie chart in Figure 1 describes the region-wise investment in renewable energy (in billion U.S. dollars). China occupies first place with the highest investment of 273.2 billion USD followed by Europe (134.4 billion USD) and the United States (92.9 billion USD) (Chang et al., 2022; Yang & Aydin, 2001). There were also significant investments from other regions, such as Asia and Oceania (excluding China and India) at 45.4 billion USD, Brazil at 22.5 billion USD, and the Middle East and Africa at 12.9 billion USD. Investment from other regions includes the Americas (except the U.S. and Brazil) with 25.4 billion USD and India with 12.4 billion USD. These numbers tell of the prior investment made by China and that there is a huge presence of developed nations in investing in renewable energies. Electrical grids cannot ensure stability when renewable resource production remains unpredictable. Imbalances may cause either excess in generation or shortage in energy supply whenever there is a mismatch between energy output and consumer demand. Unlike conventional fossil fuel-based power plants that can be adjusted according to demand requirements, managing renewable energies necessitated some new balancing techniques for effective distribution systems (Mittal & Kushwaha, 2024c; Østergaard et al., 2020; Uyar & Beşikci, 2017). The challenges faced by the power grids, if unfurnished with adequate forecasting and management techniques, may lead to inefficiencies in their operations that could further undermine widespread usage of these renewable solutions. This situation underscores the pivotal role played by artificial intelligence (AI) and machine learning (ML) in improving energy management practices. AI technologies accurately forecast shifts in availability of renewable resources, thus providing an opportunity for grid operators to pre-emptively adjust units for storage solutions and overall distribution efficiency. Analysis of very large datasets with information from both history and real-time data will surpass traditional methodologies as machine learning algorithms filter actionable knowledge to produce sound decisions, contributing to the greater reliability of the grid (Ghanbari et al., 2021; E. Hossain & Fredj, 2021; Kruppa et al., 2012). Deep learning architectures based on ANN and LSTM networks have shown superior modeling capabilities for complex, time-dependent energy data amongst many machine-learning techniques. In this study, a successfully integrated ANN-LSTM model is presented to forecast renewable energy production and optimize distribution. ANN is notably efficient in describing nonlinearities inherent in the data, while LSTM has an immense facility to track sequence patterns and long-range dependencies. This integration of these architectures improves the quality of prediction and thus confidence in the energies reported. By capturing both longer-term patterns and instantaneous oscillations in energy, the model builds a very valid platform for renewable energy management. The adoption of renewable energy across different regions varies, primarily due to infrastructure and policy support, different economic arrangements, and geographical benefits. One regional authority transitioned to a renewable energy primary power source while a few others are battling obstacles to integrate renewables with legacy ones. A data-derived investigation sometimes shows the marked differences in energy transition among the regions and communizes such processes that can work as an efficient reconciliation mechanism for the supply and demand side. Smoothing the power curve and bringing in these strategies into AI-driven projections can provide a better and more resilient energy supply system (Boriratrit et al., 2023; Chen et al., 2023; Mittal & Kushwaha, 2024a). The present work goes beyond technical tools to develop a sustainable and resilient energy future. It does contribute to renewable energy management via AI integration, not least by solving operational hurdles but also by shaping a smarter, more efficient energy ecosystem. AI will be essential in the required global transition towards renewable energy solutions by forecasting, energy balancing, and grid optimization. It must induce less reliance upon fossil energies, thus reducing climate change urgencies (Ledley et al., 1999; Murgod et al., 2025; Solomon et al., 2009). The methodology and data preprocessing steps are presented in further sections, together with model development, energy balancing, global trend identification, and successive performance evaluation. Underpinning this holistic approach is a central vision of how AI-based solutions can provide for renewable energy systems usage, grid reliability, and hence a fast-towards-cleaner and sustainable energy future (Chu et al., 2012; Shih & Tseng, 2014). 2. Methodology A systematic approach that brought together data preprocessing, model development, and performance evaluation in developing an AI system is adopted for the stabilization of the grid and balancing of renewable energy sources. This approach has ensured that the model captures changes in the output of renewable energy in the short-term to medium-term and the long-term trend while remaining within the limits of grid stability. Therefore, it started on an extended data preprocessing stage to be precise and consistent, followed by selecting integrated ANN-LSTM architecture so as to optimize energy forecasting (Garg et al., 2025; Marzouq et al., 2018; Premalatha & Valan Arasu, 2016). The model was then trained and tuned using several optimization techniques. Moreover, an energy balancing strategy was proposed and a global trend analysis was done to understand local diversity in renewable energy adoption followed too. Following that, a set of robust and validated metrics was used to assess the reliability, validity, and accuracy of the model (B Ramsundar, 2018; Candanedo et al., 2018; Liu et al., 2021; Mittal & Kushwaha, 2024b; Varoquaux et al., 2015). 2.1 Data Preprocessing Data preprocessing was conducted to ensure the accuracy and consistency of the model. The dataset was thus normalized and scaled to keep regularity among different energy sources so that one would not affect the model training process disproportionately. Missing values in the dataset were treated to avoid disturbances to time-series patterns. A time-series decomposition was applied to extract seasonality, trends, and residuals, which allowed the model to capture more complex variations in renewable energy generation. 2.2 Model Selection and Architecture Considering the fact that renewable energy generation is fluctuating in nature, an integrated ANN-LSTM networks is used. This integrated architecture helped the model to learn historical patterns as well as short-term variations, leading to more trustworthy and accurate energy estimations. The LSTM helps to show long-term dependencies and trends over the generation and consumption of energy, while ANN aided in the acquisition of nonlinear relationships and optimized efficiency in energy balance strategies. 2.3 Training and Optimization Adam optimizer was used while training the model, with Mean Squared Error (MSE) as the primary loss function. To fine-tune hyperparameters such as batch size, learning rate, and the number of hidden layers a planned grid search was carried out. The dataset was classified into training, validation, and testing (70-15-15 respectively) sets so that a balanced learning process is ensured. Then the model effectively captured the intricacies of renewable energy generation by optimizing these parameters. 2.4 Global Renewable Energy Trend Analysis Apart from predictions, the model is specific to analysing global renewable energy patterns in detail. Looking closer at global transitions in the energy framework for several nations, some essential patterns concerning renewable energy usage and associated modern structural facilities were recognized. Such investigations made it possible to grasp differences in energy production and storage capacities and the grid systems in other regions. This information can aid governments and energy suppliers devise informed plans to promote quicker global shifts to renewable energy sources. 2.5 Performance Evaluation and Validation In order to evaluate the performance of the model, several metrics were implemented. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) gave a measure of how accurate the predictions were. In addition, the R² Score evaluated how well the predicted energy generation values correlated with the actual energy generation values. A sensitivity analysis was done to check the strength of the model against extreme scenarios and abrupt changes in renewable energy generation. Such selection allowed to validate the accuracy of the model while ensuring its usefulness for real-world energy problems, which increases the reliability of the grid and optimally uses renewable sources of energy. 3. Results The findings from this study present a complete overview of trends in renewable energy production and correlations of regional contributions through historical data analysis and future forecasting. These visualizations outline the timelines and seasonal patterns for solar, wind, and hydroelectric power, generating insights crucial for their development, interdependence, and spatial distribution. The correlation analysis brings forth interdependencies among different renewables, and that helps in efficient grid management. It gives an insight into the expected change for renewable energy generation estimated up to 2050 in line with global energy forecasts, shedding light on the areas of further investment and expansion. These findings are highlighted by the figures below and elaborate a data-centric approach toward underlining some of the interactions with renewable energy production. The line chart in figure 3 shows how renewable energy is produced using three sources that are, solar, wind, and hydro during the specified time frame. The graph shows the changes of each energy source throughout the years. For example, solar and wind power has gradually been increasing, while hydro energy seems to have ups and downs. This kind of visualization makes it easy to compare and analyse how much each renewable source impacts total energy produced and tracks the change of renewable energy over the years. Figure 4 (Heatmap) presents a correlation analysis for the Indian energy generation features, for solar, wind, and hydro generation. The shades of cooler colours to warmer colours reveal the strength and direction of correlations between variable pairs. The correlation near 1 indicates positive correlation, meaning increased production of one energy source is reflected in increased production of another source, while that closer to the –1 value indicates negative correlation or an inverse relationship. The importance of this analysis arises from drawing out dependencies or synergies between different renewables through model design and grid balancing activities. By the visualization presented bubble chart in figure 5, it is clear that certain regions show a clear dominance of energy type, while others might be more balanced through solar, wind, and hydro power contributions. Comparison of any region becomes easy by taking into consideration the relative position of the bubbles, hence depicting gradually changing patterns of renewable energy adoptions. Since the bubbles are set to be alpha=0.6 transparent, even overlapping energy sources could be visualized rather well although one will be able to gauge the share or contribution of each renewable source to the issued renewable power. This way of visualization gives coherent, intuitive displays of renewable energy trends, balancing highlights on the leading production regions and those needing an earnest investment aimed at increasing renewable energy alternatives. The findings of this analysis aid in policy making, resource allocation, and designing grid stability-enhancing tools to steer the transition towards sustainable energy sources. The radar plot in figure 6 indicates the seasonal variation of each source of energy that could be renewable, such that comparisons can be drawn. The lines are oriented in any one direction and show the periods of largest intensity across Winter, Spring, Summer, and Autumn for a given energy resource. The chart calls out that certain energy resources, like solar, reach maximum levels in Summer, while others like wind or hydro may move according to climatic conditions. The overlaps among the lines give an idea of the interconnectedness of various energy sources during different seasons, while the filled regions show the more conspicuous trends. Hence, this invention provides a better idea on how energy is distributed seasonally, enabling the management and planning of resources in a more efficient manner for green energy production. Figures 7 a, b and c show three world maps of the distribution of sources of generation of renewable energy (the solar, wind, and hydro) in different countries with focus on instances where their values are more than 0. Because the energy values had very large variances, they had to first be transformed by a logarithmic function using log1p and a quantile classification scheme to afford all three generation types, due to scale, the quaint colour gradients in YlGnBu, OrRd, and PuBu colormap styles respectively. Dark tones mean generally more energy generation, while light tones translate to lesser energy output, thus giving the chance to see spatial differences in renewable energy capacity clearly. This approach effectively isolates critical differences in energy generation pinpointing either strong renewable energy contributions or regions greatly in need of further work. In figure 8, the plot portrayed survey and predicted renewable energy generation trends for solar, wind, and hydro for historically until 2021 and predictions further upto 2050. The lines carrying historical data are in solid lines while dashed lines are meant to show future predictions predicted in the model. A dashed dotted line links real values and forecasted trends in such a way as to offer a smooth appearance. These visualizations (Figure 9 a, b and c) depict the foretold global distribution of solar, wind, and hydro-electric power generation in 2050. Maps describe global-based variation of renewable energy expansion, utilizing past data and projected growth rates. The shaded colour denotes a logarithmic scale to represent energy generations. Superior scales have been anticipated for specific regions in the near future. This analysis presents some understanding of energy futures and areas in which sustainable energy investments can happen. 4. Policy Implementation and Future Perspective 4.1 Policy Implementation Effective integration of renewable energy into the energy grid necessarily requires strong policies directing further development of forecasting techniques, grid stability, and long-term sustainability. Governments and regulatory bodies government enable this transition by introducing policies requiring AI-driven energy forecasting for efficient management of the grid (M. R. Hossain et al., 2023; Magadum et al., 2025; Mittal & Kushwaha, 2024c; Rose, 1990). Utilities should also be investing in utility-scale storage technologies, including battery technologies and pumped hydro storage, to address concerns about the variable output of solar and wind power. Smart grid and cabling projects that incorporate AI optimization techniques are also set to modernize these infrastructures and aid real-time load balancing that prevents grid instability. Open-access energy datasets and partnerships between research institutes and industries can also improve forecasting accuracy and drive innovation in renewable energy management forward. Additionally, regulation has to evolve to allow dynamic energy pricing and energy trading between producers and consumers so that a viable and sustainable economy of energy can exist (Green & Stern, 2017; Hasan et al., 2023; Sherif et al., 2005). 4.2 Future Perspective Clean energy adoption at an expanding rate would use the emerging technologies and high-performance models in an AI throne for a smarter energy future, enabling energy forecasting grid stability. Hybrid AI models with cognitive enhancement would strengthen forecasting abilities in accordance with temporal changes in load demand and load consumption patterns developed using techniques like deep learning, reinforcement learning, and federated learning (Nie et al., 2024; Paletta et al., 2023; Schmitz et al., 2023). The feature of integration into quantum computing signifies tremendous possibility for processing enormous energy datasets and staying optimal with predictive modeling speedily with expert approaches. In addition, the new developments in energy grids with IoT sensor integration will bring with it real-time monitoring and penning out real-time measures for efficient energy usage. Global policy frameworks and global cooperation will be a must to enhance international power exchange and energy-sharing projects. Climate-adaptive AI models for long-term planning and resilience would be built fittingly considering climate change effects on renewable energy dispatch (Abbasi & El Hanandeh, 2016; Gallo et al., 2022; Wang et al., 2020). 5. Conclusions Adopting Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks for forecasting renewable energies and managing stability of the grid is an effective method for dealing with the concerns that come along with the unpredictable energy generation. Using the modern techniques of deep learning, the model is able to capture not only historical trends but also short-term deviations and accurately predicts renewable energy output. The AI-enabled mechanism for energy balancing guarantees better surplus energy utilization and grid stability by making use of active power control for the purpose of load levelling. Moreover, the analysis of global trends of renewable energy provides an important overview of the changes in energy possibilities of particular regions and helps in offering recommendations for adoption of sustainable strategies. The results prove that AI forecasting and balancing systems can greatly increase the effectiveness of renewable energy harnessing and simultaneously decrease usage of traditional power sources. The results were assessed utilizing MAE, RMSE, R² Score, and through rigorous model evaluation, tested the credibility of the methods used. Lastly, adding real time grid optimization and improving energy storage integration can scope of AI in developing sustainable energy solutions. This study highlights the prowess of AI in energy. References Abbasi, M., & El Hanandeh, A. (2016). 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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-6091069","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":419719747,"identity":"4cde3f42-e1b7-437c-9174-73ade6d4cf79","order_by":0,"name":"Sanjana Murgod","email":"","orcid":"","institution":"KLE Technological University","correspondingAuthor":false,"prefix":"","firstName":"Sanjana","middleName":"","lastName":"Murgod","suffix":""},{"id":419719748,"identity":"19b16194-7a3d-45cb-8ffd-076814dcb1d7","order_by":1,"name":"Kartik Garg","email":"","orcid":"","institution":"Guru Gobind Singh Indraprastha University","correspondingAuthor":false,"prefix":"","firstName":"Kartik","middleName":"","lastName":"Garg","suffix":""},{"id":419719749,"identity":"8ce87da0-6372-4565-aaf3-0e3888c00bcb","order_by":2,"name":"Triveni Magadum","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Triveni","middleName":"","lastName":"Magadum","suffix":""},{"id":419719750,"identity":"8be5f97f-e804-46c5-b32b-26098053cfe4","order_by":3,"name":"Vivek Yadav","email":"","orcid":"","institution":"Pro H2Vis Solutions","correspondingAuthor":false,"prefix":"","firstName":"Vivek","middleName":"","lastName":"Yadav","suffix":""},{"id":419719752,"identity":"5dcb649b-8dc6-456b-bdd8-4ec9df5ce42d","order_by":4,"name":"Harshit Mittal","email":"","orcid":"","institution":"Pro H2Vis Solutions","correspondingAuthor":false,"prefix":"","firstName":"Harshit","middleName":"","lastName":"Mittal","suffix":""},{"id":419719751,"identity":"899990f7-0661-4244-872c-c6dab16c49a0","order_by":5,"name":"Omkar Kushwaha","email":"data:image/png;base64,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","orcid":"","institution":"Indian Institute of Technology Madras","correspondingAuthor":true,"prefix":"","firstName":"Omkar","middleName":"","lastName":"Kushwaha","suffix":""}],"badges":[],"createdAt":"2025-02-23 15:58:33","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6091069/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6091069/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77602013,"identity":"b4197856-36e6-4897-96cc-c9e8e089ffa9","added_by":"auto","created_at":"2025-03-03 13:06:29","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":77913,"visible":true,"origin":"","legend":"\u003cp\u003eInvestment in renewable energy in billion U.S. Dollars (Chang et al., \u0026nbsp;2022; Yang \u0026amp; Aydin, 2001)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6091069/v1/762577e6cb96c37ff7a0e8cb.jpeg"},{"id":77602012,"identity":"b991b4d0-b524-47da-84d7-12f1a6154775","added_by":"auto","created_at":"2025-03-03 13:06:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":71642,"visible":true,"origin":"","legend":"\u003cp\u003eMethodology flowchart (B Ramsundar, \u0026nbsp;2018; Candanedo et al., 2018; Liu et al., 2021; Mittal \u0026amp; Kushwaha, 2024b; \u0026nbsp;Varoquaux et al., 2015).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6091069/v1/e050cece2f9997fc3fedc8ba.png"},{"id":77601611,"identity":"8d249117-8fef-4428-8693-d9e15ae03c49","added_by":"auto","created_at":"2025-03-03 12:58:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":80432,"visible":true,"origin":"","legend":"\u003cp\u003eRenewable energy generation trends over time\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6091069/v1/390bb9335e4b4d6bddb4c95f.png"},{"id":77602015,"identity":"2829614c-669a-4629-8d82-fb18c8f34392","added_by":"auto","created_at":"2025-03-03 13:06:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":153969,"visible":true,"origin":"","legend":"\u003cp\u003eFeature correlation heatmap\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6091069/v1/6b25496ba1ccd712a7460c6a.png"},{"id":77601618,"identity":"afd32886-7aa5-4578-ae3b-4f0141d0e06b","added_by":"auto","created_at":"2025-03-03 12:58:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":181940,"visible":true,"origin":"","legend":"\u003cp\u003eTop 10 renewable energy producing regions\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6091069/v1/39eec5515a166dfe78cde43c.png"},{"id":77602018,"identity":"f4236d3b-bbea-4756-a83c-6ad0a7637d36","added_by":"auto","created_at":"2025-03-03 13:06:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":237120,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal trends in renewable energy generation.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6091069/v1/f93e449117dd3efeed993c70.png"},{"id":77603274,"identity":"5d3dc2b1-5cad-41f7-b642-4a00d460d1e5","added_by":"auto","created_at":"2025-03-03 13:14:29","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":990187,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Global renewable energy generation map - Solar\u003c/p\u003e\n\u003cp\u003e(b) Global renewable energy generation map - Wind\u003c/p\u003e\n\u003cp\u003e(c) Global renewable energy generation map - Hydro\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6091069/v1/26739439ab9e3c4020c97435.png"},{"id":77601631,"identity":"a7845028-c87d-4e56-a74e-e75a9295eb35","added_by":"auto","created_at":"2025-03-03 12:58:29","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":75511,"visible":true,"origin":"","legend":"\u003cp\u003eProjected renewable energy generation trends (2022-2050).\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6091069/v1/d1c484a66b90ee8387c05287.png"},{"id":77603608,"identity":"a30cbcbe-dcd4-40e7-8c77-05a77d7dc5c0","added_by":"auto","created_at":"2025-03-03 13:22:29","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":512370,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Global renewable energy predictions for 2050 – Solar.\u003c/p\u003e\n\u003cp\u003e(b) Global renewable energy predictions for 2050 – Wind.\u003c/p\u003e\n\u003cp\u003e(c) Global renewable energy projections for 2050 – Hydro.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6091069/v1/a5ba2e91f15133701397fc42.png"},{"id":77604773,"identity":"98ac21a7-7add-40c1-8ba4-373406fb16be","added_by":"auto","created_at":"2025-03-03 13:30:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2598196,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6091069/v1/a7fa3238-b798-439f-b921-15dfa3c721e4.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAI Powered Renewable Energy Balancing, Forecasting and\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGlobal Trend Analysis using ANN-LSTM Integration\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe global shift towards renewable energy has become increasingly crucial, motivated by an urgent need to address climate change, exhaust fossil fuel supplies, and satisfy the rising demand for energy worldwide. The use of traditional energy sources such as coal, oil, and natural gas has significantly contributed to escalated carbon emissions and environmental harm. In response to these challenges, governments, industries, and researchers are expediting the transition to cleaner and more sustainable forms of energy like solar, wind, or hydroelectric power (Milani et al., 2020; Renn\u0026eacute;, 2022; Rout et al., 2025). Nevertheless, despite their vast potential benefits, renewable energy options come with distinct challenges related to their inconsistency and dependence on weather conditions. However, such uncertainties render optimal stability and reliability of the electricity supply very difficult to achieve; solar power generation changes with various weather patterns, while wind energy relies on various degrees of wind speed (Khalilnejad \u0026amp; Riahy, 2014; Salman \u0026amp; Teo, 2003; Zhang \u0026amp; Wan, 2014).\u003c/p\u003e\n\u003cp\u003eThe pie chart in \u003cstrong\u003eFigure 1\u003c/strong\u003e describes the region-wise investment in renewable energy (in billion U.S. dollars). China occupies first place with the highest investment of 273.2 billion USD followed by Europe (134.4 billion USD) and the United States (92.9 billion USD) (Chang et al., 2022; Yang \u0026amp; Aydin, 2001). There were also significant investments from other regions, such as Asia and Oceania (excluding China and India) at 45.4 billion USD, Brazil at 22.5 billion USD, and the Middle East and Africa at 12.9 billion USD. Investment from other regions includes the Americas (except the U.S. and Brazil) with 25.4 billion USD and India with 12.4 billion USD. These numbers tell of the prior investment made by China and that there is a huge presence of developed nations in investing in renewable energies.\u003c/p\u003e\n\u003cp\u003eElectrical grids cannot ensure stability when renewable resource production remains unpredictable. Imbalances may cause either excess in generation or shortage in energy supply whenever there is a mismatch between energy output and consumer demand. Unlike conventional fossil fuel-based power plants that can be adjusted according to demand requirements, managing renewable energies necessitated some new balancing techniques for effective distribution systems (Mittal \u0026amp; Kushwaha, 2024c; \u0026Oslash;stergaard et al., 2020; Uyar \u0026amp; Beşikci, 2017). The challenges faced by the power grids, if unfurnished with adequate forecasting and management techniques, may lead to inefficiencies in their operations that could further undermine widespread usage of these renewable solutions.\u0026nbsp;\u003cbr\u003eThis situation underscores the pivotal role played by artificial intelligence (AI) and machine learning (ML) in improving energy management practices. AI technologies accurately forecast shifts in availability of renewable resources, thus providing an opportunity for grid operators to pre-emptively adjust units for storage solutions and overall distribution efficiency. Analysis of very large datasets with information from both history and real-time data will surpass traditional methodologies as machine learning algorithms filter actionable knowledge to produce sound decisions, contributing to the greater reliability of the grid (Ghanbari et al., 2021; E. Hossain \u0026amp; Fredj, 2021; Kruppa et al., 2012). Deep learning architectures based on ANN and LSTM networks have shown superior modeling capabilities for complex, time-dependent energy data amongst many machine-learning techniques. In this study, a successfully integrated ANN-LSTM model is presented to forecast renewable energy production and optimize distribution. ANN is notably efficient in describing nonlinearities inherent in the data, while LSTM has an immense facility to track sequence patterns and long-range dependencies. This integration of these architectures improves the quality of prediction and thus confidence in the energies reported. By capturing both longer-term patterns and instantaneous oscillations in energy, the model builds a very valid platform for renewable energy management. The adoption of renewable energy across different regions varies, primarily due to infrastructure and policy support, different economic arrangements, and geographical benefits. One regional authority transitioned to a renewable energy primary power source while a few others are battling obstacles to integrate renewables with legacy ones. A data-derived investigation sometimes shows the marked differences in energy transition among the regions and communizes such processes that can work as an efficient reconciliation mechanism for the supply and demand side. Smoothing the power curve and bringing in these strategies into AI-driven projections can provide a better and more resilient energy supply system (Boriratrit et al., 2023; Chen et al., 2023; Mittal \u0026amp; Kushwaha, 2024a).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe present work goes beyond technical tools to develop a sustainable and resilient energy future. It does contribute to renewable energy management via AI integration, not least by solving operational hurdles but also by shaping a smarter, more efficient energy ecosystem. AI will be essential in the required global transition towards renewable energy solutions by forecasting, energy balancing, and grid optimization. It must induce less reliance upon fossil energies, thus reducing climate change urgencies (Ledley et al., 1999; Murgod et al., 2025; Solomon et al., 2009). The methodology and data preprocessing steps are presented in further sections, together with model development, energy balancing, global trend identification, and successive performance evaluation. Underpinning this holistic approach is a central vision of how AI-based solutions can provide for renewable energy systems usage, grid reliability, and hence a fast-towards-cleaner and sustainable energy future (Chu et al., 2012; Shih \u0026amp; Tseng, 2014).\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cp\u003eA systematic approach that brought together data preprocessing, model development, and performance evaluation in developing an AI system is adopted for the stabilization of the grid and balancing of renewable energy sources. This approach has ensured that the model captures changes in the output of renewable energy in the short-term to medium-term and the long-term trend while remaining within the limits of grid stability. Therefore, it started on an extended data preprocessing stage to be precise and consistent, followed by selecting integrated ANN-LSTM architecture so as to optimize energy forecasting (Garg et al., 2025; Marzouq et al., 2018; Premalatha \u0026amp; Valan Arasu, 2016). The model was then trained and tuned using several optimization techniques. Moreover, an energy balancing strategy was proposed and a global trend analysis was done to understand local diversity in renewable energy adoption followed too. Following that, a set of robust and validated metrics was used to assess the reliability, validity, and accuracy of the model (B Ramsundar, 2018; Candanedo et al., 2018; Liu et al., 2021; Mittal \u0026amp; Kushwaha, 2024b; Varoquaux et al., 2015).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1 Data Preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData preprocessing was conducted to ensure the accuracy and consistency of the model. The dataset was thus normalized and scaled to keep regularity among different energy sources so that one would not affect the model training process disproportionately. Missing values in the dataset were treated to avoid disturbances to time-series patterns. A time-series decomposition was applied to extract seasonality, trends, and residuals, which allowed the model to capture more complex variations in renewable energy generation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Model Selection and Architecture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsidering the fact that renewable energy generation is fluctuating in nature, an integrated ANN-LSTM networks is used. This integrated architecture helped the model to learn historical patterns as well as short-term variations, leading to more trustworthy and accurate energy estimations. The LSTM helps to show long-term dependencies and trends over the generation and consumption of energy, while ANN aided in the acquisition of nonlinear relationships and optimized efficiency in energy balance strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Training and Optimization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdam optimizer was used while training the model, with Mean Squared Error (MSE) as the primary loss function. To fine-tune hyperparameters such as batch size, learning rate, and the number of hidden layers a planned grid search was carried out. The dataset was classified into training, validation, and testing (70-15-15 respectively) sets so that a balanced learning process is ensured. Then the model effectively captured the intricacies of renewable energy generation by optimizing these parameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Global Renewable Energy Trend Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApart from predictions, the model is specific to analysing global renewable energy patterns in detail. Looking closer at global transitions in the energy framework for several nations, some essential patterns concerning renewable energy usage and associated modern structural facilities were recognized. Such investigations made it possible to grasp differences in energy production and storage capacities and the grid systems in other regions. This information can aid governments and energy suppliers devise informed plans to promote quicker global shifts to renewable energy sources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Performance Evaluation and Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to evaluate the performance of the model, several metrics were implemented. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) gave a measure of how accurate the predictions were. In addition, the R\u0026sup2; Score evaluated how well the predicted energy generation values correlated with the actual energy generation values. A sensitivity analysis was done to check the strength of the model against extreme scenarios and abrupt changes in renewable energy generation. Such selection allowed to validate the accuracy of the model while ensuring its usefulness for real-world energy problems, which increases the reliability of the grid and optimally uses renewable sources of energy.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe findings from this study present a complete overview of trends in renewable energy production and correlations of regional contributions through historical data analysis and future forecasting. These visualizations outline the timelines and seasonal patterns for solar, wind, and hydroelectric power, generating insights crucial for their development, interdependence, and spatial distribution. The correlation analysis brings forth interdependencies among different renewables, and that helps in efficient grid management. It gives an insight into the expected change for renewable energy generation estimated up to 2050 in line with global energy forecasts, shedding light on the areas of further investment and expansion. These findings are highlighted by the figures below and elaborate a data-centric approach toward underlining some of the interactions with renewable energy production.\u003c/p\u003e\n\u003cp\u003eThe line chart in figure 3 shows how renewable energy is produced using three sources that are, solar, wind, and hydro during the specified time frame. The graph shows the changes of each energy source throughout the years. For example, solar and wind power has gradually been increasing, while hydro energy seems to have ups and downs. This kind of visualization makes it easy to compare and analyse how much each renewable source impacts total energy produced and tracks the change of renewable energy over the years.\u003c/p\u003e\n\u003cp\u003eFigure 4 (Heatmap) presents a correlation analysis for the Indian energy generation features, for solar, wind, and hydro generation. The shades of cooler colours to warmer colours reveal the strength and direction of correlations between variable pairs. The correlation near 1 indicates positive correlation, meaning increased production of one energy source is reflected in increased production of another source, while that closer to the \u0026ndash;1 value indicates negative correlation or an inverse relationship. The importance of this analysis arises from drawing out dependencies or synergies between different renewables through model design and grid balancing activities.\u003c/p\u003e\n\u003cp\u003eBy the visualization presented bubble chart in figure 5, it is clear that certain regions show a clear dominance of energy type, while others might be more balanced through solar, wind, and hydro power contributions. Comparison of any region becomes easy by taking into consideration the relative position of the bubbles, hence depicting gradually changing patterns of renewable energy adoptions. Since the bubbles are set to be alpha=0.6 transparent, even overlapping energy sources could be visualized rather well although one will be able to gauge the share or contribution of each renewable source to the issued renewable power. \u0026nbsp;This way of visualization gives coherent, intuitive displays of renewable energy trends, balancing highlights on the leading production regions and those needing an earnest investment aimed at increasing renewable energy alternatives. The findings of this analysis aid in policy making, resource allocation, and designing grid stability-enhancing tools to steer the transition towards sustainable energy sources.\u003c/p\u003e\n\u003cp\u003eThe radar plot in figure 6 indicates the seasonal variation of each source of energy that could be renewable, such that comparisons can be drawn. The lines are oriented in any one direction and show the periods of largest intensity across Winter, Spring, Summer, and Autumn for a given energy resource. The chart calls out that certain energy resources, like solar, reach maximum levels in Summer, while others like wind or hydro may move according to climatic conditions. The overlaps among the lines give an idea of the interconnectedness of various energy sources during different seasons, while the filled regions show the more conspicuous trends. Hence, this invention provides a better idea on how energy is distributed seasonally, enabling the management and planning of resources in a more efficient manner for green energy production.\u003c/p\u003e\n\u003cp\u003eFigures 7 a, b and c show three world maps of the distribution of sources of generation of renewable energy (the solar, wind, and hydro) in different countries with focus on instances where their values are more than 0. Because the energy values had very large variances, they had to first be transformed by a logarithmic function using log1p and a quantile classification scheme to afford all three generation types, due to scale, the quaint colour gradients in YlGnBu, OrRd, and PuBu colormap styles respectively. Dark tones mean generally more energy generation, while light tones translate to lesser energy output, thus giving the chance to see spatial differences in renewable energy capacity clearly. This approach effectively isolates critical differences in energy generation pinpointing either strong renewable energy contributions or regions greatly in need of further work.\u003c/p\u003e\n\u003cp\u003eIn figure 8, the plot portrayed survey and predicted renewable energy generation trends for solar, wind, and hydro for historically until 2021 and predictions further upto 2050. The lines carrying historical data are in solid lines while dashed lines are meant to show future predictions predicted in the model. A dashed dotted line links real values and forecasted trends in such a way as to offer a smooth appearance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese visualizations (Figure 9 a, b and c) depict the foretold global distribution of solar, wind, and hydro-electric power generation in 2050. Maps describe global-based variation of renewable energy expansion, utilizing past data and projected growth rates. The shaded colour denotes a logarithmic scale to represent energy generations. Superior scales have been anticipated for specific regions in the near future. This analysis presents some understanding of energy futures and areas in which sustainable energy investments can happen.\u003c/p\u003e"},{"header":"4. Policy Implementation and Future Perspective","content":"\u003cp\u003e\u003cstrong\u003e4.1 Policy Implementation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEffective integration of renewable energy into the energy grid necessarily requires strong policies directing further development of forecasting techniques, grid stability, and long-term sustainability. Governments and regulatory bodies government enable this transition by introducing policies requiring AI-driven energy forecasting for efficient management of the grid (M. R. Hossain et al., 2023; Magadum et al., 2025; Mittal \u0026amp; Kushwaha, 2024c; Rose, 1990). Utilities should also be investing in utility-scale storage technologies, including battery technologies and pumped hydro storage, to address concerns about the variable output of solar and wind power. Smart grid and cabling projects that incorporate AI optimization techniques are also set to modernize these infrastructures and aid real-time load balancing that prevents grid instability. Open-access energy datasets and partnerships between research institutes and industries can also improve forecasting accuracy and drive innovation in renewable energy management forward. Additionally, regulation has to evolve to allow dynamic energy pricing and energy trading between producers and consumers so that a viable and sustainable economy of energy can exist (Green \u0026amp; Stern, 2017; Hasan et al., 2023; Sherif et al., 2005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Future Perspective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClean energy adoption at an expanding rate would use the emerging technologies and high-performance models in an AI throne for a smarter energy future, enabling energy forecasting grid stability. Hybrid AI models with cognitive enhancement would strengthen forecasting abilities in accordance with temporal changes in load demand and load consumption patterns developed using techniques like deep learning, reinforcement learning, and federated learning (Nie et al., 2024; Paletta et al., 2023; Schmitz et al., 2023). The feature of integration into quantum computing signifies tremendous possibility for processing enormous energy datasets and staying optimal with predictive modeling speedily with expert approaches. In addition, the new developments in energy grids with IoT sensor integration will bring with it real-time monitoring and penning out real-time measures for efficient energy usage. Global policy frameworks and global cooperation will be a must to enhance international power exchange and energy-sharing projects. Climate-adaptive AI models for long-term planning and resilience would be built fittingly considering climate change effects on renewable energy dispatch (Abbasi \u0026amp; El Hanandeh, 2016; Gallo et al., 2022; Wang et al., 2020).\u0026nbsp;\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eAdopting Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks for forecasting renewable energies and managing stability of the grid is an effective method for dealing with the concerns that come along with the unpredictable energy generation. Using the modern techniques of deep learning, the model is able to capture not only historical trends but also short-term deviations and accurately predicts renewable energy output. The AI-enabled mechanism for energy balancing guarantees better surplus energy utilization and grid stability by making use of active power control for the purpose of load levelling. Moreover, the analysis of global trends of renewable energy provides an important overview of the changes in energy possibilities of particular regions and helps in offering recommendations for adoption of sustainable strategies. The results prove that AI forecasting and balancing systems can greatly increase the effectiveness of renewable energy harnessing and simultaneously decrease usage of traditional power sources. The results were assessed utilizing MAE, RMSE, R\u0026sup2; Score, and through rigorous model evaluation, tested the credibility of the methods used. Lastly, adding real time grid optimization and improving energy storage integration can scope of AI in developing sustainable energy solutions. This study highlights the prowess of AI in energy.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbbasi, M., \u0026amp; El Hanandeh, A. (2016). 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A wind-hydrogen energy storage system model for massive wind energy curtailment. \u003cem\u003eInternational Journal of Hydrogen Energy\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(3), 1243\u0026ndash;1252. https://doi.org/10.1016/j.ijhydene.2013.11.003\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Indian Institute of Technology Madras","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Grid Stability, Renewable Energy Balancing, ANN-LSTM Integration, Global Renewable Energy Forecasting, Machine Learning in Energy","lastPublishedDoi":"10.21203/rs.3.rs-6091069/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6091069/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe instability of renewable energy sources like solar and wind places significant hurdles on energy distribution and grid stability, thus hampering the race towards sustainable energy solutions. These instabilities, mainly due to fluctuating weather conditions, may lead to surpluses or shortages of energy-with inevitable effects on the grid's reliability. It is proposed that an AI-enabled system based on ANN and LSTM solutions be developed to analyse global energy trends, predict renewable generation accurately, and enhance the grid's resilience. The new model resides on the historical and real-time energy data and adequately captures the long-range transition of energy and the short-range fluctuations in energy, allowing better energy management. Along with that, the intelligent forecasting will also optimize energy storage and minimize the overreliance on normal fossil fuel energy. The insights drawn out by this model provide considerable assistance to decision-makers, energy suppliers, and grid operators in their drive for a more stable, efficient, dependable, and sustainable energy infrastructure. This research highlights the significant role that AI-driven predictive analytics should play in facilitating global transitions toward renewable energy while addressing some of the critical operational challenges to grid reliability and energy distribution.\u003c/p\u003e","manuscriptTitle":"AI Powered Renewable Energy Balancing, Forecasting and\nGlobal Trend Analysis using ANN-LSTM Integration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-03 12:58:24","doi":"10.21203/rs.3.rs-6091069/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"19b75897-632c-45c4-aee8-7a59c2ba0a19","owner":[],"postedDate":"March 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44745437,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2025-03-03T12:58:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-03 12:58:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6091069","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6091069","identity":"rs-6091069","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}