Abstract
One of the major factors that lead to energy losses for utility distribution systems is electricity or energy theft. Energy theft is tampering with smart meter reading to reduce customer energy usage and reduce electricity bills. A thief customer tends to consume more energy and hence, the theft negatively affects the power supply quality in the form of transformer overload, voltage unbalance, and voltage drop on system buses. Meanwhile, it also causes great economic losses for the business of electric utility. In order to enable efficient energy theft detection, data-driven approaches including utilizing trained deep neural networks are proposed in this paper. The machine learning approaches can detect energy theft involving stealthy connections or meter tampering at the level of smart meters or aggregated levels. In this work, the detection effectiveness of different approaches is evaluated on real case study data at the end consumer level. The challenges of class imbalance and the missing values (around 25% of the whole fields) are addressed in the LSTM-based methodology. In this paper, results are obtained on real energy consumption data to show the higher performance of the proposed solutions compared to previously presented work.