Solar Power Forecasting Using Hybrid Deep Learning: Performance Enhancement with Random Forest-BiLSTM and Ensemble Modeling

Accurate solar power forecasting is critical for grid integration and operational stability of renewable energy systems. This study presents a hybrid deep learning ensemble approach to predict solar generation by capturing complex temporal dependencies in irradiance data. Five hybrid architectures were evaluated: RF-BiLSTM, CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-Transformer, each combining convolutional or recurrent components to extract spatial and sequential features from historical time series. The RF-BiLSTM model achieved the best individual performance with R² = 0.6568, MAE = 30,728 W, and MSE = 1.81 × 10⁹ W². An ensemble model integrating the top three architectures using inverse MAE-weighted averaging demonstrated superior performance with R² = 0.6933, MAE = 28,809.89 W, and MSE = 1.53 × 10⁹ W², reducing prediction error by 6.2% compared to the best individual model. The proposed ensemble framework effectively balances model strengths, enhances forecast robustness, and provides a scalable, data-driven solution for renewable energy forecasting in smart grid and energy management systems.