Research On Public Building Energy Consumption Prediction Method Based On NAR Neural Network Prediction Technology

Abstract

In order to solve the problem of high energy consumption of public buildings and optimize and improve energy conservation of public buildings, we built a building energy consumption prediction model based on NAR neural network prediction technology improved by BP neural network algorithm, and the energy consumption value is predicted. The large public buildings as the research object, the key factors to determine the effect of building energy consumption and collect the corresponding data processing, as the input parameters of neural network prediction public buildings energy consumption value, according to the actual situation will eventually NAR prediction of neural network and BP network prediction method and the comparative analysis the measured data. The results show that NAR neural network can predict the energy consumption of public buildings more accurately than BP neural network under different building parameters.

Explanatory Optimization of the Prediction Model for Building Energy Consumption

“…For models with low data requirements, the trend of the time series for building energy consumption prediction can be obtained through simple statistical analysis of time nodes. en, it is possible to make the nal prediction of building energy consumption [8][9][10][11][12][13][14][15][16][17]. Traditional prediction models, which are based on arti cial neural networks (ANNs), consider the various factors a ecting building energy consumption comprehensively.…”