Modeling of solar energy potential in Libya using an artificial neural network model

Abstract

In this work, we develop an artificial neural network model to predict the potential of solar power in Libya. We use multilayered, feed-forward, back-propagation neural networks for the mean monthly solar radiation using the data of 25 cities spread over Libya for the period of 6 years (2010-2015). Meteorological and geographical data (longitude, latitude, and altitude, month, mean sunshine duration, mean temperature, and relative humidity) are used as input to the network. The solar radiation is in the output layer of the network. The results show that the correlation coefficients between the ANN predictions and actual mean monthly global solar radiation for training and testing datasets are higher than 98%. Hence, the predictions from ANN model in locations where solar radiation data are not available has a high reliability.