Electricity Behavior Modeling and Anomaly Detection Services Based on a Deep Variational Autoencoder Network

Understanding electrical load profiles and detecting anomaly behaviors are important to the smart grid system. However, current load identification and anomaly analysis are based on static analysis, and less consideration is given to anomaly findings under load change conditions. This paper proposes...

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Vydáno v:Energies (Basel) Ročník 17; číslo 16; s. 3904
Hlavní autoři: Lin, Rongheng, Chen, Shuo, He, Zheyu, Wu, Budan, Zou, Hua, Zhao, Xin, Li, Qiushuang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.08.2024
Témata:
Algorithms
anomaly detection
Approximation
autoencoder network
Automation
China
Classification
Clustering
Customers
Data analysis
Data compression
Data mining
Electric power systems
Electricity
Electricity distribution
Energy consumption
Energy management
Forecasting
load pattern
Methods
Neural networks
Parameter estimation
Role models
China
ISSN:1996-1073, 1996-1073
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Shrnutí:Understanding electrical load profiles and detecting anomaly behaviors are important to the smart grid system. However, current load identification and anomaly analysis are based on static analysis, and less consideration is given to anomaly findings under load change conditions. This paper proposes a deep variational autoencoder network (DVAE) for load profiles, along with anomaly analysis services, and introduces auto-time series data updating strategies based on sliding window adjustment. DVAE can help reconstruct the load curve and measure the difference between the original and the newer curve, whose measurement indicators include reconstruction probability and Pearson similarity. Meanwhile, the design of the sliding window strategy updates the data and DVAE model in a time-series manner. Experiments were carried out based on datasets from the U.S. Department of Energy and from Southeast China. The results showed that the proposed services could result in a 5% improvement in the AUC value, which helps to identify the anomaly behavior.
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ISSN:1996-1073
1996-1073
DOI:10.3390/en17163904