Research on High-Precision Load Forecasting Model Based on Improved Metaheuristic Algorithms and Multi-Physics Coupling Feature Optimization

Research on High-Precision Load Forecasting Model Based on Improved Metaheuristic Algorithms and Multi-Physics Coupling Feature Optimization | 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 Article Research on High-Precision Load Forecasting Model Based on Improved Metaheuristic Algorithms and Multi-Physics Coupling Feature Optimization Weiyan Xu, Qinglong Ma, Jiyong Xu, Shaohua Jiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7279081/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 load forecasting is imperative for ensuring stable operation of power grids with high-penetration renewable energy. Targeting the dynamic load characteristics of the Zhangjiakou region, this study proposes a random forest framework (RF-AOA) integrating multi-physics feature engineering and an improved metaheuristic algorithm. Key innovations include: Multi-physics Coupling Mechanism Decoding: A four-dimensional temporal decomposition (hourly fluctuation/daily periodicity/weekly trend/monthly evolution) disentangles nonlinear interactions among temperature, wind speed, solar radiation, and humidity, first revealing solar radiation as the dominant driver (feature weight: 2.6) > hourly features (1.6) > temperature (1.3). Kent-Chaos Optimized AOA (KC-AOA): Chaotic initialization (entropy: 0.943) and adaptive exploration-exploitation balancing (23% higher exploration probability) achieve breakthrough hyperparameter optimization for random forests (decision tree depth: 200 layers). Validation using 11,000-hour data from Zhangjiakou demonstrates:RF-AOA attains R²=0.977 (12.3% improvement), MAE=0.057, RMSE=0.117；95% of prediction errors constrained within ±7.5%；37% accuracy boost under radiation mutation scenarios (>50 W/(m²·h))；Critical engineering value: Every 10% reduction in forecast error elevates regional dispatch efficiency by 18% and reduces peak-valley difference by 23%, significantly enhancing renewable energy utilization. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Multi-physics coupling Random Forest (RF) Metaheuristic algorithm Arithmetic Optimization Algorithm (AOA) Full Text Additional Declarations No competing interests reported. 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. 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-7279081","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":523915210,"identity":"6eea73a2-6219-4142-8447-97b564cdebc4","order_by":0,"name":"Weiyan Xu","email":"","orcid":"","institution":"Honghe Power Supply Bureau of Yunnan Power Grid Company","correspondingAuthor":false,"prefix":"","firstName":"Weiyan","middleName":"","lastName":"Xu","suffix":""},{"id":523915212,"identity":"2ab83309-39ca-4492-9a19-06c67bbc376f","order_by":1,"name":"Qinglong 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