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A Hybrid K-Means-LSTM-XGBoost Framework for Low False-Positive Electricity Theft Detection in Power Distribution Networks

Year: 2026

Type: article

Abstract: Electricity theft causes approximately $96 billion in annual global losses, threatens grid stability, and creates public safety hazards. Existing detection methods suffer from unacceptably high false positive rates (10–15%), making them impractical for real-world deployment where wrongful accusations carry high operational and reputational costs. This paper proposes a novel hybrid framework, K-MeansLSTM-XGBoost (K-MLX), that integrates unsupervised clustering, deep learning-based temporal feature extraction, and ensemble classification to address this critical gap.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.20287723

Authors: Rohit Anil Nawale, Prof. Pritish P. Bisne, Sameer L. Karpe, Ratnesh A. Patil, Bhavesh A. Manekar, Gauri R. Tohare

Language: English

Topic: Electricity Theft Detection Techniques

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W7161715422

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