Improving Energy Theft Detection through Time Series Segmentation and Ensemble Learning

Non-technical loss and energy theft detection are crucial for improving the stability and reducing financial losses in smart grid and power grid utilities. Recently, the availability of massive datasets has improved detection capabilities using sophisticated techniques like deep neural networks. However, training models on extensive feature sets, such as multi-year data, can lead to confusion due to varied behavioral changes in electricity consumption. To address this, we propose a reformulation of the energy theft detection problem by segmenting the time series data and training individual models on each segment. These models’ anomaly scores are then aggregated to produce a final classification. Our framework has shown significant improvement, elevating the F1 score from 0.6 to 0.74, outperforming recent state-of-the-art techniques on the SGCC dataset, the only publicly available dataset labeled for energy theft.