Multi-objective optimization prediction model for building parameters of photovoltaic windows based on NSGA II-BP

Abstract

This article simulates the indoor useful daylight illuminance (UDI), energy consumption, and power generation of photovoltaic (PV) window buildings using EnergyPlus simulation software. Extensive data on these simulation parameters are obtained using parametric simulation software and combined with actual meteorological data. The factors significantly influencing PV window building performance are determined based on ANOVA. A model is developed to predict energy consumption, power generation, and UDI of PV window buildings using a back propagation neural network. For better lighting quality, lower energy consumption, and greater power generation, NSGA-II is introduced to optimize the windows' performance with multi-objective parameters. Moreover, the resulting energy saving rate, annual average power generation growth rate, and UDI growth rate are compared with the initial values to evaluate the effectiveness of the optimal solution. The results demonstrate that the energy saving rate of the building is 18.23 %, and the growth rates of the useful daylight illuminance and power generation reach 41.6 % and 5.12 %, respectively, compared to the initial values.