Multi-step ahead forecasting of global solar radiation for arid zones using deep learning

Abstract

Solar irradiance is fluctuating and intermittent in nature. In order to optimally harness solar energy, this variability needs to be accounted for. Forecasting solar radiation proves to be helpful in optimal design, and operation of solar-energy based systems. This paper presents a solar irradiance forecasting scheme for multi-horizon forecasting of solar radiation considering 3/6/24 hours ahead scenarios. The proposed model uses long short term memory network, considering the dependence between hours of the same day along with other variables such as: direct horizontal irradiance, direct normal irradiance, relative humidity, dew point, temperature, wind speed, and wind direction. Solar radiations for four different locations of the Thar desert region have been forecasted. The model is optimized in terms of number of neurons and is evaluated using standard statistical indicators: RMSE and MAPE. RMSE for four different locations varied in the range of 0.099 to 0.181, along with MAPE values, which range from 6.79% to 10.47%. Low values of RMSE and MAPE indicate the efficacy of the proposed model.