Estimation of global solar radiation using adaptive neuro-fuzzy inference system

D. Benatiallah, K. Bouchouicha, A. Benatiallah, A. Harouz, B. Nasri


Abstract: In this paper, a hybrid intelligent system technique, named adaptive neuro-fuzzy inference system (ANFIS) has been used to estimate global solar radiation models under all sky condition. fifteen ANFIS based models were developed based on astronomical and meteorological parameters, namely, average air temperature (Tavg), relative humidity (RH), declination (DE), hour angle (HA) and extraterrestrial solar irradiation (H0), during one year at four meteorological stations across different climatic regions of Algeria were considered. Models efficiency were evaluated using statistical tests, including mean bias error (MBE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2). The results showed that, the model 2 in the first scenario with input temperature average, relative humidity, hour angle and using the function gbellmf MFs offered the best combination for predicting global solar radiation compared to other models in all stations. This model can be used for heating, cooling and designing solar energy systems in arid and semi-arid climatic region when data are available.

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