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"

1 Parvin K, Lipu M S H, Hannan M A, et al. Intelligent controllers and optimization algorithms for building energy management towards achieving sustainable development: challenges and prospects[J]. IEEE Access, 2021, 9: 41577-41602.
2 Dun M, Wu L. Forecasting the building energy consumption in China using grey model[J]. Environmental Processes—an International Journal, 2020, 7(3): 1009-1022.
3 Daut M M, Hassan M Y, Abdullah H, et al. Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: a review[J]. Renewable & Sustainable Energy Reviews, 2017, 70: 1108-1118.
4 Amber K P, Ahmad R, Aslam M W, et al. Intelligent techniques for forecasting electricity consumption of buildings[J]. Energy, 2018, 157: 886-893.
5 吴炜洪, 许巧玲, 严哲钦, 等. GM-LSSVM模型在建筑能耗预测中的应用[J]. 福州大学学报: 自然科学版, 2017, 45(2): 238-245.
Wu Wei-hong, Xu Qiao-ling, Yan Zhe-qin, et al. Forecasting of building energy consumption based on grey theory and least squares support vector machine[J]. Journal of Fuzhou University(Natural Science Edition), 2017, 45(2): 238-245.
6 陈彦熹, 刘建华, 李旭东, 等. 基于ANN的绿色办公建筑HVAC系统运行能耗预测[J]. 建筑节能, 2017, 45(10): 1-5.
Chen Yan-xi, Liu Jian-hua, Li Xu-dong, et al. HVAC system energy consumption prediction of green office building based on ANN method[J]. Building Energy Efficiency, 2017, 45(10): 1-5.
7 丁飞鸿, 刘鹏, 卢暾, 等. 基于遗传优化决策树的建筑能耗短期预测模型[J]. 计算机工程, 2019, 45(6): 280-289, 296.
Ding Fei-hong, Liu Peng, Lu Tun, et al. Short-term prediction model of building energy consumption based on genetically optimized decision tree[J]. Computer Engineering, 2019, 45(6): 280-289, 296.
8 汪君, 吴利瑞. 基于BP神经网络模型的上海高校能耗预测研究[J]. 建筑节能, 2015, 43(1): 92-97.
Wang Jun, Wu Li-rui. Forecast of the energy consumpt ion in universit ies at Shanghai based on BP neural networks[J]. Building Energy Efficiency, 2015, 43(1): 92-97.
9 王定奥, 刘清惓, 戴伟, 等. 基于BP神经网络的空调能耗预测与监控系统[J]. 现代电子技术, 2019, 42(22): 140-144.
Wang Ding-ao, Liu Qing-quan, Dai Wei, et al. Air⁃conditioning energy consumption prediction and monitoring system based on BP neural network[J]. Modern Electronics Technique, 2019, 42(22): 140-144.
10 陈昭明, 邹劲松, 王伟, 等. 改进粒子群神经网络融合有限元分析的铸锻双控动态成型多目标优化[J/OL]. (2021-04-27).
GN=5CWkbd0DujMK38znX6J2h%2bLBTn4%3d
11 黄宝洲, 杨俊华, 卢思灵, 等. 基于改进粒子群优化神经网络算法的波浪捕获功率预测[J]. 太阳能学报, 2021, 42(2): 302-308.
Huang Bao-zhou, Yang Jun-hua, Lu Si-ling, et al. Wave capture power forecasting based on improved particle swarm optimization neural network algorithm[J]. Acta Energiae Solaris Sinica, 2021, 42(2): 302-308.
12 倪红海, 郭献洲. 基于GA-BP神经网络的电机铸铝转子压铸工艺优化[J]. 热加工工艺, 2021(15): 61-63, 66.
Ni Hong-hai, Guo Xian-zhou. Optimization of die-casting process of motor casting aluminum rotor based on GA-BP neural network[J]. Hot Working Technology, 2021(15): 61-63, 66.
13 吕栋, 欧吉坤, 于胜文. 基于MEA-BP神经网络的卫星钟差预报[J]. 测绘学报, 2020, 49(8): 993-1003.
Lv Dong, Ji-kun Ou, Yu Sheng-wen. Prediction of the satellite clock bias based on MEA-BP neural network[J]. Acta Geodaetica et Cartograghica Sinica, 2020, 49(8): 993-1003.
14 黄伟, 常俊, 孙智滨. 基于MEA-BP神经网络的压气机特性曲线预测[J]. 重庆理工大学学报: 自然科学, 2019, 33(2): 67-74.
Huang Wei, Chang Jun, Sun Zhi-bin. Characteristic curve prediction of compressor based on MEA-BP neural network[J]. Journal of Chongqing University of Technology(Natural Science), 2019, 33(2): 67-74.
15 唐立力, 陈国彬. 基于MEA优化BP神经网络的农机滚动轴承故障诊断[J]. 农机化研究, 2019, 41(3): 214-218.
Tang Li-li, Chen Guo-bin. Fault diagnosis for rolling bearing of agricultural mechanical based on BP neural network optimized by mind evolutionary algorithm[J]. Journal of Agricultural Mechanization Research, 2019, 41(3): 214-218.
16 孙俊, 唐凯, 毛罕平, 等. 基于MEA-BP神经网络的大米水分含量高光谱技术检测[J]. 食品科学, 2017, 38(10): 272-276.
Sun Jun, Tang Kai, Mao Han-ping. Hyperspectral detection of moisture content in rice based on MEA-BP neural network[J]. Food Science, 2017, 38(10): 272-276.
17 李晓英, 苏志伟, 田佳乐, 等. 基于GRA-MEA-BP耦合模型的城市需水预测研究[J]. 水资源与水工程学报, 2018, 29(1): 50-54.
Li Xiao-ying, Su Zhi-wei, Tian Jia-le. Research on GRA-MEA-BP coupling model for water demand prediction[J]. Journal of Water Resources＆ Water Engineering, 2018, 29(1): 50-54.
18 马佩. 公共建筑能源效率分析及管理系统设计[D]. 武汉: 华中科技大学土木与水利工程学院, 2017.
Ma Pei. Energy efficiency analysis and management system design of public buildings[D]. Wuhan: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, 2017.
19 陈磊, 王江锋, 谷远利, 等. 基于思维进化优化的多源交通数据融合算法[J]. 吉林大学学报: 工学版, 2019, 49(3): 705-713.
Chen Lei, Wang Jiang-feng, Gu Yuan-li, et al. Multi-source traffic data fusion algorithm based on mind evolutionary algorithm optimization[J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 705-713.
20 张通鑫. 通风空调系统能耗监测管理平台研究[D]. 武汉: 华中科技大学土木与水利工程学院, 2018.
Zhang Tong-xin. Study of energy consumption monitoring and management platform of ventilation and air conditioning system[D]. Wuhan: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, 2018.
21 吕炳霖. 基于RS-485的抄表系统硬件实现及采集成功率的数据分析[D]. 济南: 山东大学信息科学与工程学院, 2017.
Lv Bing-lin. Data analysis of hardware implementation and acquisition success rate of meter reading system based on RS-485[D].Ji'nan: School of Information Science and Engineering, Shandong University, 2017.
22 Kim W J, Gwak D G, Kim D H. Development of DSP-based modbus communication scheme and control module for controlling actuators in industrial equipment[J]. Transactions of the Korean Society of Mechanical Engineers A, 2017, 41(11): 1109-1117.
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