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Smart modeling of photovoltaic energy production based on meteorological data and production capacity: Utilizing advanced machine learning algorithms | ||
| Biosystems Engineering and Renewable Energies | ||
| دوره 1، شماره 1، فروردین 2025، صفحه 70-74 اصل مقاله (362.89 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22069/bere.2025.23125.1014 | ||
| نویسندگان | ||
| Mohammad Hajian* ؛ Tayyeb Nazghelichi | ||
| Department of Biosystems Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran | ||
| چکیده | ||
| This study investigates the prediction of photovoltaic (PV) energy production using advanced machine learning algorithms, leveraging meteorological data and production capacity from 300 residential PV plants in Sydney, Australia. The dataset was processed into daily values to account for weather variability, and three machine learning models, i.e., random forest regression (RFR), support vector regression (SVR), and light gradient boosting regression (LightGBR), were implemented. Following rigorous preprocessing and hyperparameter optimization, LightGBR exhibited superior predictive performance, achieving a coefficient of determination (R²) of 0.9020, a mean absolute error (MAE) of 3.1621, and a mean squared error (MSE) of 0.1005. Compared to previous studies, the optimized LightGBR model demonstrated enhanced accuracy in PV energy forecasting, underscoring its potential for improving predictive modeling in this domain. These findings have significant implications for optimizing energy distribution, enhancing smart grid integration, and supporting decision-making in energy management systems. Accurate forecasting of PV energy output is essential for improving operational efficiency, minimizing energy waste, and advancing sustainability objectives in renewable energy management. | ||
تازه های تحقیق | ||
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| کلیدواژه‌ها | ||
| Forecasting؛ Light gradient boosting regression؛ Machine learning؛ Photovoltaic systems؛ Random forest algorithm؛ Support vector regression | ||
| مراجع | ||
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