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

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

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 statisti...

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Bibliographic Details
Main Authors: Minsung Jung, Hyeonseok Jang, Woohyeon Kwon, Jiyun Seo, Suna Park, Beomdo Park, Junseong Park, Donggeon Yu, Sangkeum Lee
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Energies
Subjects:
anomaly detection
power consumption data
industrial energy management
hybrid model
time series clustering
Online Access:https://www.mdpi.com/1996-1073/18/14/3720
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