Forecasting Hourly Solar Irradiance Using Long Short-Term Memory (LSTM) Network

Abstract

The increasing global demand for solar energy is a good indicator that it is a viable alternative to fossil energy. However, solar irradiance which is the principal component required for efficient power generation in solar plants is stochastic in nature. This is the reason why the prediction accuracy of solar irradiance for reliable electricity power output continues to be a very difficult task whether in the field of artificial intelligence (AI) or physical simulation. In this paper, Long Short-Term Memory (LSTM) Network, a variant of Recurrent Neural Network (RNN), was used to forecast hourly solar irradiance of Johannesburg city. LSTM is a deep learning network designed to overcome the vanishing and exploding gradient problems associated with a typical RNN. Ten years of historical meteorological data used in this work were obtained from Meteoblue. The simulation results obtained using the LSTM model were compared with the ones recorded using Support Vector Regression (SVR). From the results, it was observed that the LSTM network with normalized Root Mean Square Error (nRMSE) value of 3.2% performed much better than SVR using the same dataset.