A Comparative Analysis of Machine Learning Models for Anomaly Detection in Industrial Smart Meter Time-Series Data
Amirkhanova G. Aidynuly A. Adilzhanova S. Fu Y. Dina B. Alipbeki O.
February 2026Multidisciplinary Digital Publishing Institute (MDPI)
Information (Switzerland)
2026#17Issue 2
The integration of Advanced Metering Infrastructure (AMI) provides high-resolution electrical data, essential for enhancing industrial efficiency and monitoring equipment health. However, the utility of this data is frequently compromised by anomalies, underscoring the necessity for robust, automated detection methodologies. This study benchmarks three distinct categories of machine learning models: a statistical baseline (SARIMA), an unsupervised classifier (Isolation Forest), and a deep learning reconstruction model (LSTM-Autoencoder). The evaluation was conducted using a multivariate dataset acquired from bakery manufactory equipment, employing a synthetic anomaly injection framework with a 5% contamination rate. The results indicate significant challenges in accurately detecting anomalies within this dataset. The SARIMA model achieved the highest average F1-Score (0.256), slightly outperforming the Isolation Forest (0.233), while the LSTM-Autoencoder performed the poorest (0.110). Critically, all models exhibited extremely low precision (ranging from 0.074 to 0.204), indicating an unacceptably high rate of false positives. The findings suggest that standard configurations of these models struggle to differentiate between true anomalies and the inherent variability of industrial operations, highlighting the need for advanced optimization and feature engineering for practical deployment.
anomaly detection , false positives , industrial monitoring , isolation forest , LSTM autoencoder , machine learning , multivariate analysis , smart meter , time-series
Text of the article Перейти на текст статьи
Department of Artificial Intelligence and Big Data, Faculty of Information Technology and Artificial Intelligence, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
School of Data Science, Fudan University, Shanghai, 200433, China
Department of Artificial Intelligence and Big Data
School of Data Science
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026