Prediction of hourly solar radiation in Abu Musa Island using machine learning algorithms

Abstract

Accurate forecasting of renewable energy sources plays a key role in their integration into the grid. This study proposes machine learning algorithms to predict the hourly solar irradiance. Forecasting models were developed based two types of the input data. The first one uses local time, temperature, pressure, wind speed, and relative humidity as input variables of the models (N1); the second one is the time-series prediction of solar irradiance (N2) (forecasting models only use from past time-series solar radiation values to estimate the future values). For this purpose, multilayer feed-forward neural network (MLFFNN), radial basis function neural network (RBFNN), support vector regression (SVR), fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) are developed. The results demonstrated that for the N1, SVR and MLFFNN models have the maximum performance to predict the solar irradiance with R = 0.9999 and 0.9795, respectively. For the N2, SVR, MLFFNN and ANFIS models have reported the correlation coefficient more than 0.95 for the testing dataset.