Dynamic Ensemble Learning with Explainability for Photovoltaic Power Prediction

Dynamic Ensemble Learning with Explainability for Photovoltaic Power Prediction | 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 Dynamic Ensemble Learning with Explainability for Photovoltaic Power Prediction Fethi Achouri, Fouzi Harrou, Mehdi Damou, Benamar Bouyeddou, Abdelhakim Dorbane, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8899172/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 Photovoltaic (PV) power prediction is essential for integrating solar energy into the power grid and optimizing energy supply and demand management. However, accurate prediction is challenging due to the variability and intermittency of solar radiation, influenced by diverse weather conditions. This study enhances PV power prediction accuracy by evaluating various ensemble machine-learning models: CatBoost, Random Forest (RF), Gradient Boosting (GB), and XGBoost. These models are chosen for their capacity to model complex nonlinear patterns and enhance predictive accuracy through ensemble aggregation of weak learners. SHapley Additive exPlanations (SHAP) are employed to identify critical variables affecting PV power prediction, confirming solar radiation as the most significant factor. To account for time dependence and focus on essential variables, reduced dynamic models are developed. These models omit non-essential variables and incorporate lagged solar irradiation and PV power data, effectively capturing temporal dependencies and improving accuracy. Evaluation using real-world data from five PV systems in Brisbane, Australia, demonstrates that reduced dynamic models consistently outperform their static counterparts. Among the dynamic models, Gradient Boosting achieves the highest average R$^{2}$ of 0.979, followed closely by CatBoost and Random Forest at 0.977, while XGBoost achieves 0.975. In contrast, static models such as XGBoost, Random Forest, and Gradient Boosting yield lower average R$^{2}$ values of 0.9668, 0.9658, and 0.9452, respectively. These results underscore the critical influence of solar radiation on PV system performance, highlighting the effectiveness of dynamic modeling approaches in enhancing prediction precision and supporting informed decision-making in energy management strategies. Artificial Intelligence and Machine Learning Renewable Resources Gradient Boosting XGBoost power prediction photovoltaic Explainable machine learning Full Text Additional Declarations The authors declare no competing interests. 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-8899172","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593260065,"identity":"bf12ddce-9b3b-4c09-9210-ef284102f108","order_by":0,"name":"Fethi Achouri","email":"","orcid":"","institution":"University of Saida-Dr Tahar Moulay Saida","correspondingAuthor":false,"prefix":"","firstName":"Fethi","middleName":"","lastName":"Achouri","suffix":""},{"id":593260066,"identity":"4a91780c-a494-451a-9788-01ed247a691c","order_by":1,"name":"Fouzi 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