Identifying the most significant input parameters for predicting global solar radiation using an ANFIS selection procedure

by Kasra Mohammadi, Shahaboddin Shamshir , Amirrudin Kamsin , Lai, P.C., Zulkefli Mansor

Renewable and Sustainable Energy Reviews 63, 2016, pages 423–434


There are several variables that influence the global solarradiation (GSR) prediction; thus, determining the most significant parameterss an important task to  achieve accurate predictions. In this paper, adaptive neuro fuzzy inference system (ANFIS) is employed to identify the most relevant parameters for prediction of daily GSR. Three cities of Isfahan, Kerman and Tabass distributed in central and south central parts of Iran are considered as case studies.

The ANFIS process forv ariable selection includes evaluating several combinations of input parameters for three cases with1 ,2 and 3 inputs to recognize the most relevant sets. To achieve this, nine parameters of sunshine duration(n),  maximumpossible sunshine duration(N), minimum, maximum and average air temperatures(Tmin, Tmax and Tavg), relative humidity (Rh), watervap or pressure(VP), se a level pressure(P) and extrat errestrial radiation(Ho) are considered.

The results reveal that an optimum sets of inputs are notidentical for allcities due to dif-ference inclimate conditions and solarradiation characteristics. According to the results, considering the most relevant combinations of  input parameters is the more appropriate option for all cities to achieve more accuracy and less complexity in predictions.

The survey results emphasize the importance of appropriate selection of input parameters to predict daily GSR. Such suitable, simple and accurate pre- diction is profitable to properly design and evaluate the performance of solar energy systems, which subsequently leads to technical and economic benefits.

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