Next generation prediction model for daily solar radiation on horizontal surface using a hybrid neural network and simulated annealing method

Abstract

Accurate prediction of solar radiation is essential for optimal design of solar systems. This paper presents an innovative artificial intelligence approach for the determination of the daily solar radiation. A new nonlinear model was developed to predict the daily solar radiation on horizontal surface using a hybrid method coupling artificial neural network (ANN) and simulated annealing (SA), called ANN/SA. This method uses SA-based temperature cycling to improve the ANN calibration performance. A calculation procedure was presented to interpret the ANN/SA model and transform it into a practical design equation. The ANN/SA technique formulates the daily solar radiation in terms of several meteorological parameters. Thousands of daily observations during 1995–2014 in a nominal city in Iran were used to develop the solar radiation models. Validity of the model was verified through different phases. Sensitivity analysis was conducted and discussed. The ANN/SA model accurately predicts the daily solar radiation and outperforms the ANN, support vector machines (SVM), and existing regression and machine learning models.