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Machine learning-based performance prediction for continuous power generation of solar water pumping system
Abstract
Integrating photovoltaic (PV) systems with pumped-water storage (PWS) enables continuous power generation and efficient water management in off-grid applications. Despite the growing use of machine learning (ML) in renewable energy, its application in hybrid PV–PWS configurations remains limited, particularly for real-time prediction. This study develops an ML-based framework to predict the electro-hydraulic behavior of a PV–PWS system. Input features include solar irradiance, ambient temperature, wind speed, PV cell temperature, and pump flow rate, targeting hydro turbine power and water head. A 1000-sample dataset is generated via validated MATLAB numerical simulations. Five algorithms—Random Forest, SVR, AdaBoost, CatBoost, and XGBoost—are trained using Python and evaluated via R², MAE, RMSE, and MAPE. The XGBoost model achieves the highest accuracy, with R² values of 0.9768 and 0.949 for power output and water head, respectively. SHAP analysis identifies pump flow rate and solar irradiance as the most influential features. The proposed framework demonstrates ML's effectiveness for real-time prediction and operational improvement of hybrid PV–PWS systems under varying weather conditions.
Received: 10 March 2026
Accepted: 14 April 2026
Published: 28 April 2026
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DOI: https://dx.doi.org/10.21622/resd.2026.12.1.1993
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Copyright (c) 2026 Said M. A.Ibrahim, Alhasan M. Azouz, Hamdy H. El-Ghetany
Renewable Energy and Sustainable Development
E-ISSN: 2356-8569
P-ISSN: 2356-8518
Published by:
Academy Publishing Center (APC)
Arab Academy for Science, Technology and Maritime Transport (AASTMT)
Alexandria, Egypt
