Artificial intelligent based techno-economic-exergetic optimization of a thermoelectric enhanced building integrated photovoltaic thermal system

The increasing need for energy has made building integrated photovoltaic thermal (BIPV/T) systems which have the ability to provide thermal and electrical energy, become popular. In photovoltaic systems, as the temperature increases, the electrical efficiency decreases. Therefore, in this study, a BIPV/T thermoelectric system is investigated to enhance the system efficiency. The studied system was optimized using the combined optimization method based on artificial intelligence and conventional optimization methods such as Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The objective functions are produced electrical energy, recovered heat and payback period (PBP). The optimized BIPV/T thermoelectric system surpasses the BIPV/T in produced electrical energy, showing a 6.93 % improvement, but PBP is 5.16 years, which is one year (exactly 1.04 years) longer compared to the BIPV/T system. The effects of irradiance, wind speed, and inlet air temperature have been investigated on electrical and thermal exergy efficiency so that the system can be evaluated in different geographical locations. It was found that the increase of irradiance, air inlet temperature and wind speed respectively cause a decrease, decrease and increase in electrical exergy efficiency and also cause an increase, decrease and decrease in thermal exergy efficiency.