Enhance the Performance of Solar Irradiance Deep Learning Forecasting Model Using Recursive Estimation Method with Grid Search Algorithm - Kumar, Gautam; Goyal, Sandip Kumar (Distributed Generation & Alternative Energy Journal, 2026)
This study presents a better way to predict solar irradiance by combining the Recursive Estimation Method for Signal Decomposition with Bidirectional Long Short-Term Memory (BiLSTM) networks that are made for predictive modeling. As solar power plays a bigger and bigger role in India’s green energy strategy, it is very important to be able to accurately predict solar irradiance in order to make the best use of resources. The suggested RE-BiLSTM framework does better than standalone models like LSTM, GRU, and BiLSTM, as well as the CEEMDAN-BiLSTM model hybrid, at different times of day (15 minutes, 30 minutes, and 60 minutes) and in different seasons (summer, monsoon, autumn, and winter). RMSE, MAE, and R2 are some of the evaluation metrics that show the proposed model consistently has lower error rates and higher predictive accuracy, especially at shorter time scales. Comparative analysis shows that the forecasting errors are more than 50% lower than those of the other models, which shows how strong the method is. These results suggest that the RE-BiLSTM model is a promising way to improve solar irradiance prediction and help India adapt solar power into its energy infrastructure.