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Related Experiment Video

Updated: Aug 3, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Anomaly Detection in Automatic Meter Intelligence System Using Positive Unlabeled Learning and Multiple Symbolic

Thi Ngoc Anh Nguyen^1,2, Hoai Thu Vu^2,3, Minh Tuan Dang^2,3

¹Applied Mathematics Department, School of Applied Mathematics and Informatics, Hanoi University of Science and Technology, Hanoi, Vietnam.

|April 10, 2023

Summary

This summary is machine-generated.

This study introduces a machine learning approach for detecting electrical meter anomalies in smart grids. The proposed method effectively identifies unusual power consumption patterns, outperforming traditional alternatives in accuracy and speed.

Keywords:

anomaly detection multiple SAX pattern recognition positive unlabeled learning smart meter time series

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Area of Science:

Electrical Engineering
Data Science
Artificial Intelligence

Background:

Smart grids generate vast amounts of data, making manual consumption monitoring infeasible.
Detecting abnormal power consumption is critical for smart grid stability and control.

Purpose of the Study:

To propose a novel method for detecting electrical meter anomalies using pattern recognition and unsupervised learning.
To introduce a big data and machine learning framework for anomaly detection in smart grids.

Main Methods:

Utilizing machine learning algorithms to learn patterns from unlabeled data.
Developing a framework for processing and analyzing large-scale smart grid data.
Implementing time series anomaly detection techniques for electrical meter data.

Main Results:

The proposed method accurately identifies abnormal patterns in electrical meter data.
The framework demonstrates superior performance compared to expert-based alternatives.
Achieved significant improvements in both detection accuracy and processing time.

Conclusions:

Machine learning-based anomaly detection is effective for smart grid applications.
The developed framework offers a scalable and efficient solution for monitoring electrical consumption.
Time series anomaly detection provides a robust approach for identifying meter irregularities.