Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (5): 1857-1865.doi: 10.13229/j.cnki.jdxbgxb20210436

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Modeling of building energy consumption prediction based on MEA⁃BP neural network

Wen-long TENG1(),Bing-hu CONG1,Yun-kun SHANG1,Yu-chen ZHANG1,Tian BAI2()   

  1. 1.The First Hospital of Jilin University，Changchun 130021，China
    2.College of Computer Science and Technology，Jilin University，Changchun 130021，China
  • Received:2021-05-17 Online:2021-09-01 Published:2021-09-16
  • Contact: Tian BAI E-mail:tengwenlong@jlu.edu.cn;baitian@jlu.edu.cn

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Abstract:

In order to solve the problems of energy saving management， such as poor measurement accuracy of internal energy consumption， inconsistency of scientific energy consumption index and evaluation standard， and insufficient level of energy consumption management， the MEA-BP neural network predicting the building energy consumption is established. Based on the historical energy consumption data output from the energy consumption supervision platform， the BP neural network and MEA-BP neural network were trained. By comparing the actual value with predicted value on energy consumption， it is found that MEA-BP model is more accurate in predicting energy consumption. The MEA-BP model combined with energy consumption supervision platform could improve the energy saving efficiency about water， electricity， heating and gas， achieving the optimal energy consumption control and efficient use of energy.

Key words: public buildings, neural network, energy consumption supervision platform, data of energy consumption, energy consumption prediction

CLC Number: 

  • U491.1

Fig.1

Schematic diagram of MEA-BP neural network algorithm"

Fig.2

Data acquisition and data processing system"

Fig.3

Data transmission system"

Fig.4

Management system of online monitoring"

Fig.5

Convergence steps of superior subpopulation"

Fig.6

Convergence steps of temporary subpopulation"

Fig.7

Convergence steps of superior subpopulation after dissimilation"

Fig.8

Convergence steps of temporary subpopulation after dissimilation"

Fig.9

Comparison between BP and MEA-BP in predicting electricity consumption"

Fig.10

Comparison between relative error between BP neural network and MEA-BP neural network"

Fig.11

Comparison between electricity consumption in October 2019 and October 2020"

Fig.12

Comparison between water consumption in June 2019 and June 2020"

Fig.13

Comparison between electricity consumption of air conditioning in February 2019 and February 2020"

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