A Long-and Short-term Hot User Identification Method Based on Local Outlier Density

Abstract

Power systems have many users; however, there are relatively few users focused on high-quality power supplies (namely, hot users), such as external query users, electricity theft, and information fraud from billions of users. Starting from power load data, this paper postulates that abnormal electricity load users are essential components of hot users. According to the characteristics of regional power loads, this paper first builds a cross-judgment method for abnormal loads to improve the identification accuracy, including spatial and temporal abnormal load detection methods. As the spatial method, the abnormal load detection based on the local outlier factor (LOF) algorithm detects different users from similar users using several constructed dimensional features. As the temporal method, dynamic abnormal load detection based on the short-term sequential (SS) algorithm is designed to improve the LOF missing alarm rate and computing efficiency with multi-dimensional features. This also accomplished abnormal load detection from historical dimensions. A long- and short-term hot user detection method is designed based on the outlier density in response to the insufficient support of single-point abnormal loads to judge hot users. Finally, experiments on the user load data in a certain region within a year test the abnormal detection method for long- and short-term hot user detection based on local outlier density (LOD). The experimental results indicate that this method can detect abnormal loads and search hot users. The long- and short-term hot user detection method based on the LOD can meet practical needs when only using user load data and has good generalization abilities.