An advanced machine learning-based approach for accurate forecasting of solar photovoltaic energy production
Küçük Resim Yok
Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer London Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Solar energy has a strategic importance among renewable energy sources due to its high potential and environmental sustainability features. Increasing energy demand and environmental concerns necessitate accurate estimation of solar energy production. In this study, the daily energy output (kWh) of a solar photovoltaic (PV) system is estimated using real operational data obtained from the Solar Power Plant of Baykan Denim Company, located in Malatya province of Turkey, with an installed capacity of 7090.47 kWp. The dataset includes key parameters such as irradiance (Wh/m(2)), temperature (degrees C), performance ratio (%), and temperature-corrected performance ratio (%). Four regression algorithms; Linear Regression, LSTM (Long Short-Term Memory), Random Forest, and Extreme Gradient Boosting (XGBoost) were comparatively analyzed under different data splitting strategies (70-30, 80-20, and 10-fold cross-validation). The results reveal that XGBoost consistently outperforms the other algorithms, achieving the highest accuracy and lowest error values. Specifically, the XGBoost model with 10-fold cross-validation achieved MAE: 0.006628 kWh, MSE: 0.000126 (kWh)(2), RMSE: 0.011222 kWh, and R-2: 0.998277, indicating near-perfect prediction capability. These findings demonstrate the robustness of the proposed framework and highlight its potential to ensure continuity in solar energy production and support efficient energy management processes.
Açıklama
Anahtar Kelimeler
Energy production forecast, Machine learning, Renewable energy, Solar power plant
Kaynak
Signal Image and Video Processing
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
19
Sayı
17
