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

2025 , Vol. 53 >Issue 9: 117 - 126

DOI: https://doi.org/10.12141/j.issn.1000-565X.240575

Energy，Power & Electrical Engineering

Multi-Working Condition Energy Consumption Anomaly Detection Method for Office Building Lighting Sockets Based on LSTM-AE

  • CHEN Cheng ,
  • WANG Miao ,
  • WANG Xinyao ,
  • GAO Zhiming ,
  • ZHOU Xuan ,
  • YAN Junwei
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  • 1.School of Mechanical and Automotive Engineering，South China University of Technology，Guangzhou 510640，Guangdong，China
    2.Guangdong Artificial Intelligence and Digital Economy Laboratory （Guangzhou），Guangzhou 510335，Guangdong，China
    3.School of Energy and Automotive Engineering，Shunde Polytechnic University，Foshan 528300，Guangdong，China
陈城（1991—），女，博士生，主要从事建筑智能节能研究。E-mail： 202011000733@mail.scut.edu.cn

Received date: 2024-12-09

  Online published: 2025-04-21

Supported by

the Natural Science Foundation of Guangdong Province(2019A1515011518)

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Abstract

Anomaly detection of energy consumption in building lighting and socket systems can effectively improve energy efficiency. It holds significant importance for the implementation of building energy optimization measures and the realization of energy-saving management and control. Since the energy consumption of building lighting and plug load systems is heavily influenced by the random behavior of building occupants, and given the challenges posed by noisy time-series data and difficulty in feature extraction, this study proposed an unsupervised anomaly detection method that integrates operating condition classification with deep learning, aiming to enhance the accuracy and robustness of energy consumption anomaly identification. First, the decision tree algorithm was employed to classify the energy data based on attributes such as working days vs. non-working days and working hours vs. non-working hours. Then, for each identified condition, a long short-term memory autoencoder (LSTM-AE) model was constructed to detect anomalies. This model learns to reconstruct normal data and calculates the reconstruction error. By setting differentiated thresholds, it enables energy consumption anomaly detection under unlabeled data conditions. Using 578 days of hourly lighting and socket energy consumption data from an office building located in a hot-summer and warm-winter region, the study conducted model training and hyperparameter optimization experiments. Results indicate that the number of iterations, the number of neurons, and the activation function have significant effects on the model’s performance. Energy data during working days demonstrate greater stability than those on non-working days, resulting in higher detection accuracy. The proposed method achieves average precision, recall, and F1 of 91.23%, 90.87%, and 90.80%, respectively, across four typical operating conditions, demonstrating its effectiveness in detecting energy anomalies in building lighting and socket systems.

Key words： energy consumption anomaly detection; deep learning; lighting socket system; reconstruction error; unsupervised learning

Cite this article

CHEN Cheng , WANG Miao , WANG Xinyao , GAO Zhiming , ZHOU Xuan , YAN Junwei . Multi-Working Condition Energy Consumption Anomaly Detection Method for Office Building Lighting Sockets Based on LSTM-AE[J]. Journal of South China University of Technology(Natural Science), 2025 , 53(9) : 117 -126 . DOI: 10.12141/j.issn.1000-565X.240575

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