Bridging the gap between energy consumption and distribution through non-technical loss detection

The application of Artificial Intelligence techniques in industry equips companies with new essential tools to improve their principal processes. This is especially true for energy companies, as they have the opportunity, thanks to the modernization of their installations, to exploit a large amount of data with smart algorithms. In this work we explore the possibilities that exist in the implementation of Machine-Learning techniques for the detection of Non-Technical Losses in customers. The analysis is based on the work done in collaboration with an international energy distribution company. We report on how the success in detecting Non-Technical Losses can help the company to better control the energy provided to their customers, avoiding a misuse and hence improving the sustainability of the service that the company provides.Peer ReviewedPostprint (published version

Non-technical losses detection in energy consumption focusing on energy recovery and explainability

Non-technical losses (NTL) is a problem that many utility companies try to solve, often using black-box supervised classifcation algorithms. In general, this approach achieves good results. However, in practice, NTL detection faces technical, economic, and transparency challenges that cannot be easily solved and which compromise the quality and fairness of the predictions. In this work, we contextualise these problems in an NTL detection system built for an international utility company. We explain how we have mitigated them by moving from classifcation into a regression system and introducing explanatory techniques to improve its accuracy and understanding. As we show in this work, the regression approach can be a good option to mitigate these technical problems, and can be adjusted in order to capture the most striking NTL cases. Moreover, explainable AI (through Shapley Values) allows us to both validate the correctness of the regression approach in this context beyond benchmarking, and improve the transparency of our system drastically.Peer ReviewedPostprint (published version

A quality control method for fraud detection on utility customers without an active contract

Fraud detection in energy consumption has proven to be a difficult problem for current techniques. In general, the approaches used in this area are restricted to compute a fraud score for each client based on its behaviour. The problem gets much more complicated on customers with no contract, since the company does not have enough information from them to compute an accurate profile. On this paper, we introduce a semi-autonomous method that combines different machine learning algorithms and human knowledge to alleviate the lack of information to build a framework that detects fraud nimbly.Peer ReviewedPostprint (author's final draft

A quality control method for fraud detection on utility customers without an active contract

Fraud detection in energy consumption has proven to be a difficult problem for current techniques. In general, the approaches used in this area are restricted to compute a fraud score for each client based on its behaviour. The problem gets much more complicated on customers with no contract, since the company does not have enough information from them to compute an accurate profile. On this paper, we introduce a semi-autonomous method that combines different machine learning algorithms and human knowledge to alleviate the lack of information to build a framework that detects fraud nimbly.Peer Reviewe