Non-Technical Loss Detection Using Deep Reinforcement Learning for Feature Cost Efficiency and Imbalanced Dataset

Abstract

One of the problems of the electricity grid system is electricity loss due to energy theft, which is known as non-technical loss (NTL). The sustainability and stability of the grid system are threatened by the unexpected electricity losses. Energy theft detection based on data analysis is one of the solutions to alleviate the drawbacks of NTL. The main problem of data-based NTL detection is that collected electricity usage dataset is imbalanced. In this paper, we approach the NTL detection problem using deep reinforcement learning (DRL) to solve the data imbalanced problem of NTL. The advantage of the proposed method is that the classification method is adopted to use the partial input features without pre-processing method for input feature selection. Moreover, extra pre-processing steps to balance the dataset are unnecessary to detect NTL compared to the conventional NTL detection algorithms. From the simulation results, the proposed method provides better performances compared to the conventional algorithms under various simulation environments.