The feasibility of genetic programming and ANFIS in prediction energetic performance of a building integrated photovoltaic thermal (BIPVT) system

Abstract

The main motivation of this study is to evaluate and compare the efficacy of three computational intelligence approaches, namely artificial neural network (ANN), genetic programming (GP), and adaptive neuro-fuzzy inference system (ANFIS) in predicting the energetic performance of a building integrated photovoltaic thermal (BIPVT) system. This system is capable of cooling PV panels by ventilation/exhaust air in winter/summer and generating electricity. A performance evaluation criterion (PEC) is defined in this study to examine the overall performance of the considered BIPVT system. Then, the mentioned methods are used to identify a relationship between the input and output parameters of the system. The parameter PEC is considered as the essential output of the system, while the input parameters are the length, width, and depth of the duct underneath the PV panels and air mass flow rate. To evaluate the accuracy of produced outputs, two statistical indices of R2 and RMSE are used. As a result, all models presented excellent performance where the ANN model could slightly perform better performance compared to GP and ANFIS. Finally, the equations belonging to ANN and GP models are derived, and the GP presents a more suitable formula, due to its simplicity of use, simplicity of concept, and robustness.