Hybrid auto-regressive neural network model for estimating global solar radiation in Bandar Abbas, Iran

Abstract

In this paper, a neural network auto-regressive model with exogenous inputs (NN-ARX) is utilized for predicting daily horizontal global solar radiation (DHGSR). For this aim, two sets of parameters: (1) sunshine hours (n) and maximum possible sunshine hours (N), and (2) maximum ambient temperature (T max) and minimum ambient temperature (T min) collected for Bandar Abbas city of Iran are used as inputs. The efficiency of NN-ARX is compared with that of the adaptive neuro-fuzzy inference system (ANFIS), which is a robust methodology. The attained results reveal the superiority of sunshine hours as input over air temperatures so that the NN-ARX (1) and ANFIS (1) models using n and N as inputs offer higher precision than the NN-ARX (2) and ANFIS (2) models using T max and T min as inputs. Statistical results demonstrate that NN-ARX provides favorable precision and outperforms ANFIS. The relative percentage error analysis shows that the capability of the ANN-ARX (1) model in different days of the year is indeed attractive since 89.25 % of the predictions fall within the acceptable range of −10 to +10 %. The influence of introducing extraterrestrial solar radiation (H o ) as third input on the performance of the NN-ARX models is assessed. It is found that using H o provides only slight improvements on accuracy for both sunshine duration and temperature-based predictions; thus, considering H o as the third input may not be really suitable since it also brings further complexity in terms of the required inputs. The survey results prove that NN-ARX would be an efficient alternative approach to predict DHGSR.