Adaptive Neuro-Fuzzy Inference System modelling for performance prediction of solar thermal energy system

This study investigates in details the applicability of Adaptive Neuro-Fuzzy Inference System (ANFIS) approach for predicting the performance parameters of a solar thermal energy system. Experiments were conducted on the system under a broad range of operating conditions during different Canadian seasons and weather conditions. The experimental data were used for developing the ANFIS network model. This later was then optimised and applied to predict various performance parameters of the system. The predicted values were found to be in excellent agreement with the experimental data with mean relative errors less than 1% and 9% for the stratification temperatures and the solar fractions, respectively. The results show that ANFIS approach provides high accuracy and reliability for predicting the performance of energy systems. Furthermore, the ANFIS prediction results were compared against the ANNs predictions of Yaici and Entchev [Appl Therm Eng 2014; 73:1346-57]. Results showed that the ANFIS model performed slightly better than the ANNs one. However, the ANNs method provided more flexibility in terms of model implementation and computing speed capabilities. Finally, this investigation demonstrates that ANFIS is an alternative powerful and reliable method comparable to the ANNs; they can be used with confidence for predicting the performance of complex renewable energy systems. (C) 2015 Elsevier Ltd. All rights reserved.