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<p>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/m²), temperature (°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 R²: 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.</p>

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An advanced machine learning-based approach for accurate forecasting of solar photovoltaic energy production

  • 期刊论文

  • 发表日期：

    2025年11月22日

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  • Tarkan Koca,
  • Mehmet Bilal Er,
  • Büşra Kişecok

摘要

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/m²), temperature (°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 R²: 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.