Design of an Artificial Neural Network-Based Model for Prediction Solar Radiation Utilizing Measured Weather Datasets

Abstract

Forecasting solar radiation plays an important role in the field of energy meteorology, as it provides the energy value expected to be produced by the solar plants on a specific day and time of the year. In this paper, a new and reliable artificial intelligence-based model for solar radiation prediction is presented using Artificial Neural Network (ANN). The proposed model is built utilizing real atmospheric affecting measured values according to their locational weather station. In the training process, the Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) are used. The mean absolute error (MAE) and the root mean square error (RMSE) are used to evaluate the model accuracy. Results of the investigation show that the proposed model provides the lowest error rate when using the (BR) training algorithm for predicting the average daily solar radiation.