Electricity Behavior Modeling and Anomaly Detection Services Based on a Deep Variational Autoencoder Network
Abstract
:1. Introduction
- A power consumption behavior modeling and analysis algorithm based on an improved DVAE is proposed, including typical load pattern generation and anomaly probability analysis.
- The convolution layer is used to optimize the DVAE and reduce the parameter redundancy of the model so that the model can efficiently handle extensive load data.
- The sliding window is used to adapt to the periodic change in the load so that the model can automatically change the strategy of anomaly identification according to different seasons and avoid the identification error caused by the periodic change in power consumption behavior.
- Three anomaly identification criteria are proposed: reconstruction probability, Pearson similarity, and power consumption level. Experiments on public datasets in the United States and load data in Southeast China show that the proposed model can reconstruct the electricity consumption behavior well and accurately identify abnormal electricity consumption.
2. Related Work
3. Proposed Method
- (1)
- The proposed method of modeling electricity behavior involves the use of a deep variational autoencoder. The anomalous electricity behavior is detected through the reconstruction probability and other indicators, and the variational autoencoder generation model explores the typical load patterns.
- (2)
- The conversion of the electricity pattern is determined through the anomaly detection of timing development, and the restarting of the DVAE judgment model is completed via a sliding window adaptation.
4. Mining and Anomaly Detection of the Load Pattern for Industrial and Commercial Users
4.1. Problem Description
4.2. Improved Deep Variational Autoencoder (DVAE)
Algorithm 1: The anomalous power consumption behavior detection algorithm based on DVAE |
Input: Trained model Dataset to be tested Threshold Output: The result of the anomalous power consumption behavior detection. |
1: for to do 2: 3: if then 4: is identified as anomalous 5: else 6: Get the reconstruction result 7: Calculate the correlation coefficient and the mean load 8: if is too low or is anomalous then 9: is identified as anomalous 10: else 11: is identified as normal 12: end if 13: end if 14: end for |
Returns: The result of the anomalous power consumption behavior detection. |
4.3. Sliding Window Adaption
5. Experiment and Performance Evaluation
5.1. China Load Dataset Experiment
5.1.1. Dataset
5.1.2. Experiment Results
5.1.3. Comparison Experiment
5.2. US Load Dataset Experiment
5.2.1. Dataset
5.2.2. Experiment Results
5.3. Benefits to the Grid and Users
6. Conclusions and Future Work
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Date | Users | Anomalous | |
---|---|---|---|
Industry #1 | 2014/1~2015/1 | 516 | 2330 |
Industry #2 | 2014/1~2015/1 | 387 | 104 |
Industry #3 | 2014/1~2015/1 | 476 | 2821 |
Test Sample | Reconstruction Probability | Pearson Correlation | Mean Load |
---|---|---|---|
#A | 0.6779 | 0.81665 | 9.98001 |
#B | 0.3688 | 0.56609 | 16.11646 |
#C | 0.6989 | 0.88754 | 21.09903 |
#D | 0.7306 | 0.91222 | 34.97619 |
#E | 0.2670 | 0.44971 | 32.66502 |
#F | 0.7545 | 0.87056 | 11.96069 |
#G | 0.3226 | 0.50632 | 12.17643 |
Algorithm | AUC | |
---|---|---|
DVAE | 0.848 | 0.644 |
VAE | 0.781 | 0.517 |
AE | 0.596 | 0.433 |
Type | Test Samples | Anomalous Samples | Anomalous Ratio (%) |
---|---|---|---|
Warehouse | 15,334 | 2794 | 18.22% |
Business district | 16,800 | 1613 | 9.60% |
Fast service restaurant | 16,295 | 554 | 3.39% |
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Lin, R.; Chen, S.; He, Z.; Wu, B.; Zou, H.; Zhao, X.; Li, Q. Electricity Behavior Modeling and Anomaly Detection Services Based on a Deep Variational Autoencoder Network. Energies 2024, 17, 3904. https://doi.org/10.3390/en17163904
Lin R, Chen S, He Z, Wu B, Zou H, Zhao X, Li Q. Electricity Behavior Modeling and Anomaly Detection Services Based on a Deep Variational Autoencoder Network. Energies. 2024; 17(16):3904. https://doi.org/10.3390/en17163904
Chicago/Turabian StyleLin, Rongheng, Shuo Chen, Zheyu He, Budan Wu, Hua Zou, Xin Zhao, and Qiushuang Li. 2024. "Electricity Behavior Modeling and Anomaly Detection Services Based on a Deep Variational Autoencoder Network" Energies 17, no. 16: 3904. https://doi.org/10.3390/en17163904