The implementation of univariable scheme-based air temperature for solar radiation prediction: New development of dynamic evolving neural-fuzzy inference system model

Abstract

New development of dynamic evolving neural-fuzzy inference system model was proposed for modeling solar radiation based on univariable air temperature scheme. The proposed predictive model was validated against several robust heuristic regression models including multivariate adaptive regression spline, M5 model tree and least square support vector regression. Historical data of solar radiation and air temperature for two meteorological stations: Adana and Antakya located in Turkey, were investigated. The prediction results were evaluated based on several statistical metrics. The modeling is conducted based on different data division scenarios (training/testing) phases. The attained prediction results evidenced the potential of the dynamic evolving neural-fuzzy inference system over the comparable models and for all the investigated data division scenarios. In quantitative terms, dynamic evolving neural-fuzzy inference system was enhanced the solar radiation prediction capability over the multivariate adaptive regression spline, M5 model tree and least square support vector regression models (Adana-Antakya) by 20–42%, 29–47% and 19–43% based on the root mean square errors metric. The applied predictive models were compared with the field measured average monthly solar radiation values and it was found that the proposed model estimates accurately. However, the comparable models were exhibited a considerable overestimation of the monthly averaged solar radiation values and for both inspected stations specifically in the summer months (June, July and August).