Multi-step solar irradiance forecasting using Deep learning models

Abstract

Solar energy is widely regarded as a highly promising source that is both renewable and sustainable. Accurate forecasting of global horizontal irradiance (GHI) is essential for improving the real-time operation and control of photovoltaic systems. Despite its importance, research on multi-step ahead forecasting remains scarce. This study focuses on multi-step ahead GHI prediction using minute-level data collected from Bidyut Bhaban, the headquarters of the Bangladesh Power Development Board (BPDB), located in the Ramna area of Dhaka, Bangladesh. Forecast horizons of 5, 15, 30, and 60 minutes ahead are determined by utilizing several commonly used deep learning (DL) architectures. Model performance is assessed using mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R2). Experimental results show that BiGRU performs best for short-term forecasts (5 min: R2 =0.9306,15 min: R2=0.9047), while GRU excels for longer horizons (30 min: R2=0.8862,60 min: R2=0.8634). Both models demonstrate strong potential for high-frequency solar irradiance forecasting in urban environments, offering accurate and computationally efficient solutions for grid operators in Bangladesh.