Regional performance evaluation of solar combined cooling heating and power systems for household demands

Abstract

Buildings and households contribute a lot to energy usage and gas emissions. The goal of this paper is to model and study theoretically a trigeneration solar system to meet the household energy demands. Also, a reliable network is trained to forecast the functionality of the presented system under various weather conditions to replace the time-consuming simulation process. The simultaneous generation of electricity, heating, and cooling cycles based on a photovoltaic thermal system coupled with an auxiliary heater, and absorption chiller, has been investigated dynamically using TRNSYS simulation software. The result shows that warm regions like Ahwaz province have the most suitable weather condition to exploit the presented system since the highest annual efficiency of 13.27 % is achieved, and its annual power production is calculated at 28432.17 kWh which reaches its maximum in September. To get faster results, an optimized artificial neural network (ANN) is trained based on the dataset, collected during the simulation. Excellent results were achieved for a coefficient of determination in the testing stage, which ranged from R2 = 0.991–0.999. And the mean square error of the electric power production, and overall efficiency outputs are calculated at 0.872 and 1.896, respectively. In conclusion, this study has shown the capability of a forecasting tool to predict the system’s performance in various weather conditions precisely. This issue shows that an ANN development with high accuracy can be used in further studies, to reduce the calculation time in such transient systems with a heavy run time.