Non-technical losses detection using missing values' pattern and neural architecture search

The fast growth of non-technical loss (NTL) has gradually become one of the main concerns for distribution network operators (DNOs). Electricity theft which constitutes the main part of NTL not only brings losses to the DNOs, but also reduces the quality of the supply. A traditional detection method relies on utility workers' experience and consumes a large amount of manpower. Thanks to the emerging of advanced metering infrastructure (AMI), utility companies can now collect detailed data reflecting consumers' electricity usage, which enabled algorithms-based non-technical loss detection. The current data-based methods focus on the characteristics of electricity consumption thereby less efficient when dealing with rapidly changed electricity theft techniques. This article introduced a new data set, the location information of missing values, to improve the accuracy of non-technical loss detection. The relationship between missing values and electricity theft techniques is analyzed and a neural network model is built through neural architecture search (NAS). The improved model achieved an excellent Area Under Curve (AUC) value around 0.926 which verified the close link between missing values and electricity theft techniques. The nature of neural architecture search allows automatic model update which makes it a user-friendly tool even for engineers without any neural network expertise. A case study was carried out in which the missing value pattern was analyzed through Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm.