Evaluation and forecasting of solar radiation using time series adaptive neuro‐fuzzy inference system: Seoul city as a case study

Abstract

Given that solar radiation is unpredictable, an accurate solar energy prediction model must be developed. This study aimed to evaluate the changes in solar radiation over the past 37 years in Seoul city. The capability of the adaptive neuro-fuzzy inference system (ANFIS) to forecast solar radiation using chaotic time series inputs was analysed. Results demonstrate the capability of ANFIS to provide a relatively good monthly solar prediction model with a normalised root mean square error of 0.22%, a root mean square error of 55.4, and a coefficient of determination of 0.8. The Jarque–Bera test was implemented as well to test the null hypothesis for the normal distribution of standardised residual. Results support the null hypothesis with P-value = 0.222, which indicates the normal distribution of the standardised residual and its goodness. The standardised residual shows that the model can effectively predict solar radiation on a monthly basis.