Hybrid Global and Sub-domain Approach for Accurate Hourly Cooling Load Forecasting inShort, Medium, and Long-term Horizons

Hybrid Global and Sub-domain Approach for Accurate Hourly Cooling Load Forecasting inShort, Medium, and Long-term Horizons | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Hybrid Global and Sub-domain Approach for Accurate Hourly Cooling Load Forecasting inShort, Medium, and Long-term Horizons Hangyu Che, Masafumi Kinoshita, Shiyu Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7581212/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Accurate hourly cooling load forecasting is essential for optimizing HVAC system management, reducing energy consumption, and achieving sustainability goals. This paper presents the Hybrid Global and Sub-domain Approach (HGSA), an innovative methodology that integrates advanced feature engineering, domain-based data segmentation, and ensemble modeling to achieve precise cooling load predictions across short-term, medium-term, and long-term horizons. HGSA effectively addresses the challenges of dynamic cooling load patterns through model fusion and periodic pattern recognition. Validated on real-world datasets, the proposed approach demonstrates superior performance over state-of-the-art methods, including Long Short-Term Memory (LSTM), Light Gradient Boosting Machine (LightGBM), Autoregressive Model (AR), and Facebook Prophet. Its high accuracy, adaptability to existing models, and efficient deployment make HGSA a robust and practical solution for applied scenarios, such as building energy saving management, HVAC optimization, and energy strategy development. The results establish HGSA as a reliable method for precise cooling load forecasting and strategic energy planning. Hybrid Modeling Load Forecasting BEMS HVAC Control Optimization Time Series Data Forecasting Full Text Additional Declarations Competing interest reported. Author Hangyu Che has a patent pending related to the methodology described in this study, which is owned by Hitachi Ltd. All other authors declare that they have no competing financial or non-financial interests to disclose. Cite Share Download PDF Status: Posted Version 1 posted 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. 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