Revolutionizing electricity theft detection: enhanced accuracy through NILM and multi-source data fusion

Abstract Electricity theft detection seeks to thwart the illegal use of electricity, thereby safeguarding the safety and stability of the power system. Traditional methods, which typically rely on aggregated household consumption data to identify theft, often overlook the fact that household consumption is vulnerable to fluctuations in normal user behavior. This results in high false positive and false negative rates. To refine the accuracy, we propose a novel electricity theft detection method based on Non-Intrusive Load Monitoring (NILM) and multi-source data fusion. Our approach employs advanced NILM algorithms to cost-effectively extract individual appliance consumption data from aggregated power signals. We then integrate this data with household aggregate consumption data through a multi-source data fusion architecture. By analyzing the unique consumption patterns of different types of appliances, our approach identifies theft behaviors that cannot be detected by aggregate consumption data alone. Experimental results across three real-world datasets demonstrate that our method significantly outperforms single-source data-based benchmarks, achieving up to a 7.92% gain in F1-score and a 12.6% gain in Precision. Moreover, our method exhibits strong generalization ability across a series of typical machine learning models.