Prediction and comparison of solar radiation using improved empirical models and Adaptive Neuro-Fuzzy Inference Systems

Solar radiation plays an important role in climate change, energy balance and energy applications. In this work, an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) is proposed and compared with Expanded-Improved Bristow-Campbell Model (E-IBCM) and Improved Yang Hybrid Model (IYHM) to predict daily global solar irradiance (H-g) in China. The BCM is expanded by adding meteorological parameters and coefficients calibrated at each station, the YHM is improved by correcting cloud transmittance co-efficients at three stations in Hunan province, China. Daily sunshine duration (S), relative humidity (RH), precipitation (P-re); air pressure (AP), daily mean/maximum/minimum temperature (Delta T/T-max/T-min) are used as inputs for model development and application, while daily H-g is the only output. Performances of different models are evaluated by Root Mean Square Errors (RMSE), Mean Absolute Errors (MAE) and Coefficient of Determination (R-2). The results indicate that the improved empirical models (E-IBCM and IYHM) provides better accuracy than the original models and the ANFIS model is proved to be superior to the E-IBCM and IYHM model in predicting H-g. The statistical results of ANFIS model range 0.59 -1.60 MJ m(-2) day(-1) and 0.42-1.21 MJ m(-2) day(-1) for RMSE and MAE, respectively. The nonlinear modeling process of ANFIS may contribute to its excellent modeling performance. (C) 2017 Elsevier Ltd. All rights reserved.