Journal of South China University of Technology(Natural Science) >

2022 , Vol. 50 >Issue 7: 144 - 154

DOI: https://doi.org/10.12141/j.issn.1000-565X.210422

Power & Electrical Engineering

Anomaly Detection of Complex Building Energy Consumption System Based on Machine Learning

  • Xuan ZHOU ,
  • Xinyao WANG ,
  • Junwei YAN ,
  • Shangpeng LEI ,
  • Liequan LIANG
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  • 1.School of Mechanical and Automotive Engineering，South China University of Technology，Guangzhou 510640，Guangdong，China
    2.School of Information，Guangdong University of Finance and Economics，Guangzhou 510320，Guangdong，China
周璇（1976-），女，副研究员，主要从事节能技术、数据挖掘等研究。

Received date: 2021-06-25

  Online published: 2022-01-22

Supported by

the Natural Science Foundation of Guangdong Province(2018A030313352)

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Abstract

The research on the anomaly detection of the operation state of complex energy consumption system is of great significance to the safe, stable, efficient and energy-saving operation of buildings. However, due to the influence of many factors, such as the variety of external environmental factors of buildings, the uncertainty of insider behavior, the complexity of equipment operation data and so on, the detection of abnormal operation state often meets some difficulties in information feature extraction, abnormal operation state definition and so on. Based on the classification of operation parameters and by using the quantitative description of uncertainty by information entropy and the adaptability of unsupervised learning, this paper proposed a secondary clustering anomaly detection method for the operation state of complex energy consumption system based on information entropy and by integrating K-means and self-organizing mapping model. According to the actual monitoring data of the central air-conditioning system of a large office building in the hot summer and warm winter area, the external parameters were clustered for the first time through K-means, and the steps such as dividing the primary working conditions, calculating the information entropy of the operating parameters of the subsystem under each working condition, and determining the abnormal operating state by secondary clustering of self-organizing mapping model were used to realize the abnormal detection of the operating state of the complex energy consumption system of the building. The results show that the average intra class anomaly detection accuracy of the method proposed in this study is 97.45% under the operation condition of single machine and 96.70% under the operation condition of two machines. In addition, this study further discussed and analyzed the relationship between the number of classes and the relevant indicators of in class anomaly rate under different working conditions. It concludes that the in class anomaly detection rate of single and dual hosts increases with the decrease of the total number of operating states in their class. This study provides a new systematic idea and method for the abnormal operation state detection of complex energy consumption system under the background of building energy big data.

Key words： complex energy consumption system; information entropy; K-means; self-organizing maps model; anomaly detection

Cite this article

Xuan ZHOU , Xinyao WANG , Junwei YAN , Shangpeng LEI , Liequan LIANG . Anomaly Detection of Complex Building Energy Consumption System Based on Machine Learning[J]. Journal of South China University of Technology(Natural Science), 2022 , 50(7) : 144 -154 . DOI: 10.12141/j.issn.1000-565X.210422

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