Feature Extraction of Anomaly Electricity Usage Behavior in Residence Using Autoencoder
Abstract
:1. Introduction
2. Background
2.1. Anomaly Detection
2.2. Autoencoder
2.3. One-Class SVM
3. Research Methods
3.1. Data Preprocessing
3.2. High-Level Feature Extraction
3.3. One-Class Classifier Training
3.4. Performance Comparison
4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
References
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Tsai, C.-W.; Chiang, K.-C.; Hsieh, H.-Y.; Yang, C.-W.; Lin, J.; Chang, Y.-C. Feature Extraction of Anomaly Electricity Usage Behavior in Residence Using Autoencoder. Electronics 2022, 11, 1450. https://doi.org/10.3390/electronics11091450
Tsai C-W, Chiang K-C, Hsieh H-Y, Yang C-W, Lin J, Chang Y-C. Feature Extraction of Anomaly Electricity Usage Behavior in Residence Using Autoencoder. Electronics. 2022; 11(9):1450. https://doi.org/10.3390/electronics11091450
Chicago/Turabian StyleTsai, Chia-Wei, Kuei-Chun Chiang, Hsin-Yuan Hsieh, Chun-Wei Yang, Jason Lin, and Yao-Chung Chang. 2022. "Feature Extraction of Anomaly Electricity Usage Behavior in Residence Using Autoencoder" Electronics 11, no. 9: 1450. https://doi.org/10.3390/electronics11091450
APA StyleTsai, C.-W., Chiang, K.-C., Hsieh, H.-Y., Yang, C.-W., Lin, J., & Chang, Y.-C. (2022). Feature Extraction of Anomaly Electricity Usage Behavior in Residence Using Autoencoder. Electronics, 11(9), 1450. https://doi.org/10.3390/electronics11091450