Prediction of Low-Energy Building Energy Consumption Based on Genetic BP Algorithm

Abstract

Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University, the analysis model scheme of energy consumption of individual buildings in the university is studied by using Back Propagation (BP) neural network to solve nonlinear problems and have the ability of global approximation and generalization. By analyzing the influence of different uses, different building surfaces and different energy-saving schemes on the change of building energy consumption, the grey correlation method is used to determine the main influencing factors affecting each building energy consumption, including uses, building surfaces and energy-saving schemes, which are used as the input of the model and the building energy consumption as the output of the model, so as to establish the building energy consumption analysis model based on BP neural network. However, in practical application, BP neural network has the defects of slow convergence and easy to fall into local minima. In view of this, this paper uses genetic algorithm to optimize the weight and threshold of BP neural network, completes the improvement of various building energy consumption analysis models, and realizes the qualitative analysis of building energy consumption. The model verification results show that the viscosity of the building energy consumption analysis model based on genetic algorithm improved BP neural network algorithm (GABP) in this paper is relatively high, which is more accurate than the results of the traditional BP neural network model, and the relative error of the analysis model is reduced from 11.56% to 8.13%, which proves that the GABP can be better suitable for the study of school building energy consumption analysis model, It is applied to the prediction of building energy consumption, which lays a foundation for the realization of carbon neutralization in the South expansion plan of Yangtze University.