A Comparative Study of Customized Algorithms for Anomaly Detection in Industry-Specific Power Data

Minsung Jung, Hyeonseok Jang, Woohyeon Kwon, Jiyun Seo, Suna Park, Beomdo Park, Junseong Park, Donggeon Yu and Sangkeum Lee ()
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Minsung Jung: Department of Computer Engineering, Hanbat National University, Daejeon 12613, Republic of Korea
Hyeonseok Jang: Department of Computer Engineering, Hanbat National University, Daejeon 12613, Republic of Korea
Woohyeon Kwon: Department of Computer Engineering, Hanbat National University, Daejeon 12613, Republic of Korea
Jiyun Seo: Department of Computer Engineering, Hanbat National University, Daejeon 12613, Republic of Korea
Suna Park: Department of Computer Engineering, Hanbat National University, Daejeon 12613, Republic of Korea
Beomdo Park: Department of Computer Engineering, Hanbat National University, Daejeon 12613, Republic of Korea
Junseong Park: Department of Computer Engineering, Hanbat National University, Daejeon 12613, Republic of Korea
Donggeon Yu: Department of Computer Engineering, Hanbat National University, Daejeon 12613, Republic of Korea
Sangkeum Lee: Department of Computer Engineering, Hanbat National University, Daejeon 12613, Republic of Korea

Energies, 2025, vol. 18, issue 14, 1-19

Abstract: This study compares and analyzes statistical, machine learning, and deep learning outlier-detection methods on real power-usage data from the metal, food, and chemical industries to propose the optimal model for improving energy-consumption efficiency. In the metal industry, a Z-Score-based statistical approach with threshold optimization was used; in the food industry, a hybrid model combining K-Means, Isolation Forest, and Autoencoder was designed; and in the chemical industry, the DBA K-Means algorithm (Dynamic Time Warping Barycenter Averaging) was employed. Experimental results show that the Isolation Forest–Autoencoder hybrid delivers the best overall performance, and that DBA K-Means excels at detecting seasonal outliers, demonstrating the efficacy of these algorithms for smart energy-management systems and carbon-neutral infrastructure

Keywords: anomaly detection; power consumption data; industrial energy management; hybrid model; time series clustering (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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