Power grids are critical infrastructure assets that face non-technical losses (NTL) such as electricity theft or faulty meters. NTL may range up to 40 of the total electricity distributed in emerging countries. Industrial NTL detection systems are still largely based on expert knowledge when deciding whether to carry out costly on-site inspections of customers. Electricity providers are reluctant to move to large-scale deployments of automated systems that learn NTL profiles from data due to the latter's propensity to suggest a large number of unnecessary inspections. In this paper, we propose a novel system that combines automated statistical decision making with expert knowledge. First, we propose a machine learning framework that classifies customers into NTL or non-NTL using a variety of features derived from the customers' consumption data. The methodology used is specifically tailored to the level of noise in the data. Second, in order to allow human experts to feed their knowledge in the decision loop, we propose a method for visualizing prediction results at various granularity levels in a spatial hologram. Our approach allows domain experts to put the classification results into the context of the data and to incorporate their knowledge for making the final decisions of which customers to inspect. This work has resulted in appreciable results on a real-world data set of 3.6M customers. Our system is being deployed in a commercial NTL detection software.
In the literature, different approaches for visualization of NTL are reported. In order to support the decision making, the visualization of the network topology on feeder level as well as load curves on transformer level is proposed in @cite_6 . In addition, the density of NTL in a 2D map is visualized in @cite_8 . For analytics in power grids as a whole, the need for novel and more powerful visualization techniques is argued in @cite_13 . The proposed approaches include heat maps and risk maps. All methods for visualization of NTL proposed in the literature focus only on 2D representations.
@cite_13 @cite_6 @cite_8
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The distribution systems need to adjust and intensify the use of new available technologies and incorporate intelligence to combat non-technical loss processes. Its detection is one of the greatest challenges to energy distribution companies. Studies and research involving load modeling or load estimation (LM LE) on power system distribution combined with smart grid functionality will help control commercial loses in electric power distribution systems. The use of algorithms is proposed for LM LE using monthly energy consumption data converted to hourly demand. This information and the remote monitoring consumption data via smart meters will be processed by a computational tool that will indicate the feeder with the greater probability of presenting non-technical losses, with the objective of locating energy fraud, directing company's investment on combating non-technical losses. In this work we expose a software solution proposal to support the detection process of non-technical losses of electricity in the Energy Company of the Quind´io region in Colombia (EDEQ by its acronym in Spanish). In the development of this prototype we used approaches for data integration and software engineering in order to implement a technical solution to improve the activities for analysis and visualization of electricity losses, according with the specifications and expectatives identified in the roles that participate in these process.