An integrated framework of robust local mean decomposition and bidirectional long short-term memory to forecast solar irradiance

Abstract

ABSTRACT An accurate forecasting of solar global horizontal irradiance (GHI) provides the estimation of future output from photovoltaic (PV) cells, resulting in guaranteed sustainable functioning of solar integrated smart grids. Traditional forecasting algorithms find it relatively hard to obtain the intrinsic non-linearity of solar GHI, leading to poor forecasting accuracy. This work offers a deep learning (DL)-based hybrid model called RLMD BILSTM that combines robust local mean decomposition (RLMD) algorithm with bidirectional long short-term memory (BILSTM) predictor. The proposed RLMD BILSTM model has been developed and evaluated for two separate Indian locations: Hisar and Jaipur, by comparing with benchmark models: persistence, long short-term memory (LSTM), gated recurrent unit (GRU), BILSTM, and their RLMD-based hybrid variations (RLMD LSTM and RLMD GRU). The performance of the proposed model has been verified by the statistical and visual results of short-term annual and seasonal forecast of solar GHI. From the results, it has been observed that the proposed model achieved lower root mean square error (RMSE) (16.34 W/m2-35.07 W/m2) compared to contrast mforecastsodels. The higher annual R2 yielding a value of 0.977–0.995 for the proposed model compared to the contrast model proved its better fitness. Moreover, the study showed that the RLMD with BILSTM improved the RMSE (59.16%–88.88%) and mean absolute error (MAE) (66.45%–92.22%) over various contrast models. The study also conducted the sensitivity analysis using the forecasting efficacy and forecast skill to prove the significance of the proposed model.