Daily Solar Radiation Forecasting for Northwest Nigeria Using Long Short-Term Memory

Abstract

In order to ensure energy security and environmental sustainability, transition to renewable energy sources is required. One of the most viable and sustainable renewable energy sources is solar. However, developing solar energy systems requires solar radiation data which is scarce for most locations including Northwest Nigeria. In order to address this challenge, solar radiation is usually estimated from the available meteorological parameters. Several previous studies have used various methods including geospatial techniques and machine learning to predict monthly and yearly solar radiation, while few studies have focused on the estimation of daily solar radiation. Meanwhile, providing daily solar radiation data is necessary for the development of solar energy systems. Deep learning has been shown to be effective in solar radiation forecasting. To evaluate the performance of the deep learning method for daily solar radiation prediction, a Long Short-Term Memory (LSTM) based deep learning model was developed in this study. The forecasting model was created using daily solar radiation data collected over a 21-year period by the Nigerian Meteorological Agency in three major towns in North West Nigeria: Kano, Kaduna, and Katsina. The model was evaluated using two statistical indicators: coefficient of determination (R2) and Root Mean Square Error (RMSE). Results showed that R2 of 0.79 and 0.78 were obtained for the training and testing datasets respectively, while RMSE of 0.46 and 0.47 were obtained for the training and testing datasets respectively. Overall, the LSTM deep learning model has been proven to be effective in forecasting daily solar radiation.