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All Journal Bulletin of Electrical Engineering and Informatics
Sanaboina Sai Srilakshmi
Joginpally B R Engineering College
Electrical & Electronics Engineering

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Empowering energy management: anomaly detection in smart meter data for proactive consumption control Batchalakuri Jyothi; Bhavana Pabbuleti; Beeravalli Mounika; Hrushitha Kalapala; Meda Uma Santhosh Chandra; Sanaboina Sai Srilakshmi; Bommasani Ganesh Babu; Kambhampati Venkata Govardhan Rao; Malligunta Kiran Kumar; Rami Reddy Chilakala
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10957

Abstract

The increasing deployment of smart energy meters (SEMs) has enabled real-time monitoring of energy consumption, but the vast data generated makes it challenging to detect anomalies that may indicate inefficiencies, faults, or unauthorized usage. This study aims to enhance energy management by developing a hybrid anomaly detection framework that improves accuracy while providing actionable insights for consumers. The proposed method integrates statistical and machine learning (ML) approaches, specifically Z-score, local outlier factor (LOF), one-class support vector machine (SVM), and isolation forest (iForest), to analyze simulated smart meter data. An anomaly is flagged only when identified by all four methods, thereby reducing false positives and improving reliability. The framework is implemented in an interactive dashboard built with streamlit, offering real-time visualization, peak-time alerts, usage forecasts, and personalized consumption suggestions. Results demonstrate that the hybrid approach outperforms single-method models, achieving higher detection accuracy and practical applicability. The findings highlight the potential of combining complementary detection techniques with proactive feedback to empower consumers, reduce energy wastage, and support sustainable energy management. This work provides a scalable foundation for future real-time deployment in smart grids and microgrid environments.
Co-Authors Batchalakuri Jyothi Beeravalli Mounika Bhavana Pabbuleti Bommasani Ganesh Babu Hrushitha Kalapala Kambhampati Venkata Govardhan Rao Malligunta Kiran Kumar Meda Uma Santhosh Chandra Rami Reddy Chilakala