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Detecting and reacting to non-technical losses (NTL) is a fundamental activity that energy providers need to face in their daily routines. This is known to be challenging since the phenomenon of NTL is multi-factored, dynamic and extremely contextual, which makes artificial intelligence (AI) and, in particular, machine learning, natural areas to bring effective and tailored solutions. If the human factor is disregarded in the process of detecting NTL, there is a high risk of performance degradation since typical problems like dataset shift and biases cannot be easily identified by an algorithm. This paper presents a case study on incorporating explainable AI (XAI) in a mature NTL detection system that has been in production in the last years both in electricity and gas. The experience shows that incorporating this capability brings interesting improvements to the initial system and especially serves as a common ground where domain experts, data scientists, and business analysts can meet., Peer Reviewed, Postprint (published version)

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 Reviewed, Postprint (published version)

Utility companies have a great interest in identifying energy losses. Here, we focus on Non-Technical Losses (NTL), which refer to losses caused by utility theft or meter errors. Typically, utility companies resort to machine learning solutions to automate and optimise the identification of such losses. This paper extends an existing NTL-detection framework: by including knowledge-based NTL segmentation, we have detected some opportunities for improving the accuracy and the explanations provided to the utility company. Our improved models focus on specific types of NTL and therefore, the explanations provided are easier to interpret, allowing stakeholders to make more informed decisions. The improvements and results presented in the article may benefit other industrial frameworks., This work has been supported by MINECO and FEDER funds under grants TIN2017-86727-C2-1-R and TIN2017-89244-R, the recognition 2017SGR-856 (MACDA) from AGAUR (Generalitat de Catalunya)., Peer Reviewed, Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura, Postprint (published version)

Machine learning algorithms applied towards detection of non-technical losses are increasingly becoming a go-to solution and tools that help detection losses in such a way are being developed by power utilities of all sizes across the globe. Non-technical losses represent the biggest fraction of distribution losses in electric power systems of all countries making utilities experience significant revenue fatalities. These losses are largely represented by fraudulent activities performed by the customers such, which lead to additional losses, damaging the infrastructure of networks and deteriorating the safety of the grid. This thesis proposes an extensive review of different methods and case studies of non-technical losses in grids in different parts of the world, their effect on the economies of scale and other aspects of social well-being such as links between the rate of fraud being committed in the national grid to human development index (HDI) and other metrics. Modern technologies of power consumption estimation and regulation such as smart meters are given a close look as well as their infrastructure, role and possible applications are also reviewed in this thesis. As for the machine learning part overview, over 80 research papers were carefully studied in order to quantify the progression of interest towards applied artificial intelligence algorithms from the industry, key performance indicators and state-of-the-art fraud detection models were analysed and findings were extracted. The findings suggest several optimal application thresholds of learning techniques under examination with respect to specifics of the problem, available resources and budgets as well as volume and quality of the data. In the experimental section of the thesis, a specific threat model was developed and effectively employed through the generation of synthetic fraudulent profiles according to a methodology presented in the literature. The underlying data that was used to satisfy the q

Non technical losses (NTL) detection plays a crucial role in protecting the security of smart grids. Employing massive energy consumption data and advanced artificial intelligence (AI) techniques for NTL detection are helpful. However, there are concerns regarding the effectiveness of existing AI-based detectors against covert attack methods. In particular, the tampered metering data with normal consumption patterns may result in low detection rate. Motivated by this, we propose a hybrid data-driven detection framework. In particular, we introduce a wide deep convolutional neural networks (CNN) model to capture the global and periodic features of consumption data. We also leverage the maximal information coefficient algorithm to analysis and detect those covert abnormal measurements. Our extensive experiments under different attack scenarios demonstrate the effectiveness of the proposed method.