@article{CHEN2025, 
author = {Cheng CHEN and Miao WANG and Xinyao WANG and Zhiming GAO and Xuan ZHOU and Junwei YAN},
title = {Multi-Working Condition Energy Consumption Anomaly Detection Method for Office Building Lighting Sockets Based on LSTM-AE},
year = {2025},
journal = {Journal of South China University of Technology (Natural Science Edition)},
volume = {53},
number = {9},
pages = {117-126},
keywords = {energy consumption anomaly detection, deep learning, lighting socket system, reconstruction error, unsupervised learning},
url = {https://www.sciopen.com/article/10.12141/j.issn.1000-565X.240575},
doi = {10.12141/j.issn.1000-565X.240575},
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.}
}