The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey

Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science. NTLs cause significant harm to the economy, as in some countries they may range up to 40% of the total electricity distributed. The predominant research direction is employing artificial intelligence to predict whether a customer causes NTL. This paper first provides an overview of how NTLs are defined and their impact on economies, which include loss of revenue and profit of electricity providers and decrease of the stability and reliability of electrical power grids. It then surveys the state-of-the-art research efforts in a up-to-date and comprehensive review of algorithms, features and data sets used. It finally identifies the key scientific and engineering challenges in NTL detection and suggests how they could be addressed in the future

A mining framework to detect non-technical losses in power utilities

This paper deals with the characterization of customers in power companies in order to detect consumption Non-Technical Losses (NTL). A new framework is presented, to find relevant knowledge about the particular characteristics of the electric power customers. The authors uses two innovative statistical estimators to weigh variability and trend of the customer consumption. The final classification model is presented by a rule set, based on discovering association rules in the data. The work is illustrated by a case study considering a real data base

Detection of Non-Technical Losses: The Project MIDAS

The MIDAS project began in 2006 as collaboration between Endesa, Sadiel, and the University of Seville. The objective of the MIDAS project is the detection of Non-Technical Losses (NTLs) on power utilities. The NTLs represent the non-billed energy due to faults or illegal manipulations in clients’ fa cilities. Initially, research lines study the application of techniques of data mining and neural networks. After several researches, the studies are expanded to other research fields: expert systems, text mining, statistical techniques, pattern recognition, etc. These techniques have provided an automated system for detection of NTLs on company databases. This system is in the test phase, and it is applied in real cases in company databases

An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

Human-aware application of data science techniques

In recent years there has been an increase in the use of artificial intelligence and other data-based techniques to automate decision-making in companies, and discover new knowledge in research. In many cases, all this has been performed using very complex algorithms (so-called black-box algorithms), which are capable of detecting very complex patterns, but unfortunately remain nearly uninterpretable. Recently, many researchers and regulatory institutions have begun to raise awareness of their use. On the one hand, the subjects who depend on these decisions are increasingly questioning their use, as they may be victims of biases or erroneous predictions. On the other hand, companies and institutions that use these algorithms want to understand what their algorithm does, extract new knowledge, and prevent errors and improve their predictions in general. All this has meant that researchers have started to focus on the interpretability of their algorithms (for example, through explainable algorithms), and regulatory institutions have started to regulate the use of the data to ensure ethical aspects such as accountability or fairness. This thesis brings together three data science projects in which black-box predictive machine learning has been implemented to make predictions: - The development of an NTL detection system for an international utility company from Spain (Naturgy). We combine a black-box algorithm and an explanatory algorithm to guarantee our system's accuracy, transparency, and robustness. Moreover, we focus our efforts on empowering the stakeholder to play an active role in the model training process. - A collaboration with the University of Padova to provide explainability to a Deep Learning-based KPI system currently implemented by the MyInvenio company. - A collaboration between the author of the thesis and the Universitat de Barcelona to implement an AI solution (a black-box algorithm combined with an explanatory algorithm) to a social science problem. The unique characteristics of each project allow us to offer in this thesis a comprehensive analysis of the challenges and problems that exist in order to achieve a fair, transparent, unbiased and generalizable use of data in a data science project. With the feedback arising from the research carried out to provide satisfactory solutions to these three projects, we aim to: - Understand the reasons why a prediction model can be regarded as unfair or untruthful, making the model not generalisable, and the consequences from a technical point of view in terms of low accuracy of the model, but also how this can affect us as a society. - Determine and correct (or at least mitigate) the situations that cause the problems in terms of robustness and fairness of our data. - Assess the difference between the interpretable algorithms and black-box algorithms. Also, evaluate how well the explanatory algorithms can explain the predictions made by the predictive algorithms. - Highlight what the stakeholder's role in guaranteeing a robust model is and how to convert a data-driven approach to solve a predictive problem into a data-informed approach, where the data patterns and the human knowledge are combined to maximize profit.En els últims anys s'ha produït un augment de l'ús de la intel·ligència artificial i altres tècniques basades en dades per automatitzar la presa de decisions en les empreses, i descobrir nous coneixements en la recerca. En molts casos, tot això s'ha realitzat utilitzant algorismes molt complexos (anomenats algorismes de caixa negra), que són capaços de detectar patrons molt complexos, però, per desgràcia, continuen sent gairebé ininterpretables. Recentment, molts investigadors i institucions reguladores han començat a conscienciar sobre el seu ús. D'una banda, els subjectes que depenen d'aquestes decisions estan qüestionant cada vegada més el seu ús, ja que poden ser víctimes de prejudicis o prediccions errònies. D'altra banda, les empreses i institucions que utilitzen aquests algoritmes volen entendre el que fa el seu algorisme, extreure nous coneixements i prevenir errors i millorar les seves prediccions en general. Tot això ha fet que els investigadors hagin començat a centrar-se en la interpretació dels seus algorismes (per exemple, mitjançant algorismes explicables), i les institucions reguladores han començat a regular l'ús de les dades per garantir aspectes ètics com la rendició de comptes o la justícia. Aquesta tesi reuneix tres projectes de ciència de dades en els quals s'ha implementat aprenentatge automàtic amb algorismes de caixa negra per fer prediccions: - El desenvolupament d'un sistema de detecció de NTL (Non-Technical Losses, pèrdues d'energia no tècniques) per a una empresa internacional del sector de l'energia d'Espanya (Naturgy). Aquest sistema combina un algorisme de caixa negra i un algorisme explicatiu per garantir la precisió, la transparència i la robustesa del nostre sistema. A més, centrem els nostres esforços en la capacitació dels treballadors de l'empresa (els "stakeholders") per a exercir un paper actiu en el procés de formació dels models. - Una col·laboració amb la Universitat de Padova per proporcionar l'explicabilitat a un sistema KPI basat en Deep Learning actualment implementat per l'empresa MyInvenio. - Una col·laboració de l'autor de la tesi amb la Universitat de Barcelona per implementar una solució d'AI (un algorisme de caixa negra combinat amb un algorisme explicatiu) a un problema de ciències socials. Les característiques úniques de cada projecte ens permeten oferir en aquesta tesi una anàlisi exhaustiva dels reptes i problemes que existeixen per a aconseguir un ús just, transparent, imparcial i generalitzable de les dades en un projecte de ciència de dades. Amb el feedback obtingut de la recerca realitzada per a oferir solucions satisfactòries a aquests tres projectes, el nostre objectiu és: - Entendre les raons per les quals un model de predicció pot considerar-se injust o poc fiable, fent que el model no sigui generalitzable, i les conseqüències des d'un punt de vista tècnic en termes de baixa precisió del model, però també com pot afectar-nos com a societat. - Determinar i corregir (o almenys mitigar) les situacions que causen els problemes en termes de robustesa i imparcialitat de les nostres dades. - Avaluar la diferència entre els algorismes interpretables i els algorismes de caixa negra. A més, avaluar com els algorismes explicatius poden explicar les prediccions fetes pels algorismes predictius. - Ressaltar el paper de les parts interessades ("Stakeholders") per a garantir un model robust i com convertir un enfocament únicament basat en les dades per resoldre un problema predictiu en un enfocament basat en les dades però complementat amb altres coneixements, on els patrons de dades i el coneixement humà es combinen per maximitzar els beneficis.Postprint (published version

A case study of improving a non-technical losses detection system through explainability

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 ReviewedPostprint (published version

Solutions for detection of non-technical losses in the electricity grid: a review

This paper is a review of literature with an analysis on a selection of scienti c studies for detection of non-technical losses. Non-technical losses occurring in the electric grid at level of transmission or of distribution have negative impact on economies, affecting utilities, paying consumers and states. The paper is concerned with the lines of research pursued, the main techniques used and the limitations on current solutions. Also, a typology for the categorization of solutions for detection of non-technical losses is proposed and the sources and possible attack/vulnerability points are identifi ed. The selected literature covers a wide range of solutions associated with non-technical losses. Of the 103 selected studies, 6 are theoretical, 25 propose hardware solutions and 72 propose non-hardware solutions. Data based classi cation models and data from consumption with high resolution are respectively required in about 47% and 35% of the reported solutions. Available solutions cover a wide range of cases, with the main limitation found being the lack of an uni ed solution, which enables the detection of all kinds of non-technical losses

A human-in-the-loop approach based on explainability to improve NTL detection

Implementing systems based on Machine Learning to detect fraud and other Non-Technical Losses (NTL) is challenging: the data available is biased, and the algorithms currently used are black-boxes that cannot be either easily trusted or understood by stakeholders. This work explains our human-in-the-loop approach to mitigate these problems in a real system that uses a supervised model to detect Non-Technical Losses (NTL) for an international utility company from Spain. This approach exploits human knowledge (e.g. from the data scientists or the company's stakeholders) and the information provided by explanatory methods to guide the system during the training process. This simple, efficient method that can be easily implemented in other industrial projects is tested in a real dataset and the results show that the derived prediction model is better in terms of accuracy, interpretability, robustness and flexibility.Peer ReviewedPostprint (author's final draft