Solar Power Prediction in Coastal Zones of Bangladesh: A Hybrid Empirical-Machine Learning Approach for the Teknaf 20 MW Solar Power Plant

This study reports a relative assessment of single machine learning, ensemble, and hybrid deep learning methods for multi-step solar radiation and power forecasting tailored to the coastal climate of Bangladesh. Astronomical solar geometry, empirical global solar radiation regression models (Angstrom-Prescott, Akinoglu and Ecevit, Ampratwum and Dorvlo, and Newland), and five core algorithms (XGBoost, Random Forest, ANN, LSTM, and SVR) are applied to the Teknaf 20 MW solar power plant in Cox’s Bazar, Bangladesh. The models predict daily and multi-step power generation using meteorological and engineered features obtained through feature selection. The empirical regression formulations successfully capture seasonal variations with high correlation coefficients (r>0.98). Among the appraised models, Random Forest Regression demonstrates the superior performance (R2 = 0.9910, RMSE = 0.1898 MW, MAPE = 4.70%, Accuracy = 95.30%), closely followed by XGBoost and SVR. These tree-ensemble and kernel-based methods provide accuracy comparable to or exceeding complex deep learning models at a low computational cost, making them highly suitable for solar power planning and grid management in tropical and monsoon regions.