TL;DR
The proposed approach can be effectively utilised to optimise tilt angle selection, improve energy forecasting, and enhance the overall efficiency of solar photovoltaic systems.
Air temperature, wind direction, wind speed, and relative humidity were the top four features affecting solar power generation identified from SHAP analysis, and the results show the potential of using machine learning techniques for accurate solar energy prediction, leading to enhanced grid management, renewable energ...
A comparative analysis of three machine learning models, namely Artificial Neural Network, Random Forest, and Support Vector Regression, for short-term solar PV power forecasting shows that Random Forest provides superior forecasting performance compared with ANN and SVR, achieving lower prediction errors and a higher...
This research provides a direct comparison of five machine learning algorithms, that is, Decision Tree Regression (DTR), Multiple Linear Regression (MLR), Random Forest (RF), k‐Nearest Neighbors (kNN), and Extreme Gradient Boosting (XGBoost) for forecasting PV power output in Jazan, Saudi Arabia.
This study demonstrates the effectiveness of gradient boosting models and neural networks for accurately forecasting solar production in Meknes, thus offering promising prospects for the deployment of solar solutions in Morocco.
A hybrid PV forecasting framework that combines stacking ensemble learning with a targeted residual correction strategy, and demonstrates that analyzing error distribution and forecasting robustness provides valuable insights beyond conventional aggregate metrics, contributing to the development of more reliable photov...
An in‐depth comparison of four state‐of‐the‐art ML models across various horizons and training data sizes shows the importance of integrating MRMR, with guidelines for researchers and energy planners to achieve more accurate and efficient solar irradiance prediction.
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