Advanced Solar Irradiance Forecasting Using Hybrid Ensemble Deep Learning and Multisite Data Analytics for Optimal Solar-Hydro Hybrid Power Plants

Abstract

Solar energy with hydropower power plants marks a significant leap forward in renewable energy innovation. The combination ensures a consistent power supply by merging the fluctuations of solar energy with the predictable storage provided by hydropower. This research aims to predict high solar irradiance on hydropower plants to maximize active power generation. A novel hybrid decomposed residual ensembling model for deep learning (SBL_TSRA_RW) using models such as autoregressive integrated moving average (ARIMA) and seasonal-trend decomposition using loess (STL) along with prediction and optimization models such as Bidirectional LSTM (Bi-LSTM), and Whale Optimization Algorithm (WOA) methods are used to predict the irradiances. Various forecasting methods, including STL-Bi-LSTM, SBL_TSA_R, SBL_TA_RS, and SBL_TSRA_R models, are assessed to determine their effectiveness in predicting solar radiation. The results show the accuracy of the proposed model, with RMSE and MAE values of 1.85 W/m² and 1.31 W/m², respectively. The proposed SBL_TSRA_RW model results are more accurate than the Bi-LSTM, STL-Bi-LSTM, SBL_TSA_R, SBL_TA_RS, and SBL_TSRA_R models, with RMSE value reductions of 517%, 217%, 151%, 98%, and 1%, respectively.