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

MIDAS: Detection of Non-technical Losses in Electrical Consumption Using Neural Networks and Statistical Techniques

Datamining has become increasingly common in both the public and private sectors. A non-technical loss is defined as any consumed energy or service which is not billed because of measurement equipment failure or ill-intentioned and fraudulent manipulation of said equipment. The detection of non-technical losses (which includes fraud detection) is a field where datamining has been applied successfully in recent times. However, the research in electrical companies is still limited, making it quite a new research topic. This paper describes a prototype for the detection of non-technical losses by means of two datamining techniques: neural networks and statistical studies. The methodologies developed were applied to two customer sets in Seville (Spain): a little town in the south (pop: 47,000) and hostelry sector. The results obtained were promising since new non-technical losses (verified by means of in-situ inspections) were detected through both methodologies with a high success rate

Detection of Non-Technical Losses in Smart Distribution Networks: a Review

With the advent of smart grids, distribution utilities have initiated a large deployment of smart meters on the premises of the consumers. The enormous amount of data obtained from the consumers and communicated to the utility give new perspectives and possibilities for various analytics-based applications. In this paper the current smart metering-based energy-theft detection schemes are reviewed and discussed according to two main distinctive categories: A) system statebased, and B) arti cial intelligence-based.Comisión Europea FP7-PEOPLE-2013-IT

Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets

Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections. Accurate prediction of NTLs for customers using machine learning is therefore crucial. To date, related research largely ignore that the two classes of regular and non-regular customers are highly imbalanced, that NTL proportions may change and mostly consider small data sets, often not allowing to deploy the results in production. In this paper, we present a comprehensive approach to assess three NTL detection models for different NTL proportions in large real world data sets of 100Ks of customers: Boolean rules, fuzzy logic and Support Vector Machine. This work has resulted in appreciable results that are about to be deployed in a leading industry solution. We believe that the considerations and observations made in this contribution are necessary for future smart meter research in order to report their effectiveness on imbalanced and large real world data sets.Comment: Proceedings of the Seventh IEEE Conference on Innovative Smart Grid Technologies (ISGT 2016

Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses?

Non-technical losses (NTL) occur during the distribution of electricity in power grids and include, but are not limited to, electricity theft and faulty meters. In emerging countries, they may range up to 40% of the total electricity distributed. In order to detect NTLs, machine learning methods are used that learn irregular consumption patterns from customer data and inspection results. The Big Data paradigm followed in modern machine learning reflects the desire of deriving better conclusions from simply analyzing more data, without the necessity of looking at theory and models. However, the sample of inspected customers may be biased, i.e. it does not represent the population of all customers. As a consequence, machine learning models trained on these inspection results are biased as well and therefore lead to unreliable predictions of whether customers cause NTL or not. In machine learning, this issue is called covariate shift and has not been addressed in the literature on NTL detection yet. In this work, we present a novel framework for quantifying and visualizing covariate shift. We apply it to a commercial data set from Brazil that consists of 3.6M customers and 820K inspection results. We show that some features have a stronger covariate shift than others, making predictions less reliable. In particular, previous inspections were focused on certain neighborhoods or customer classes and that they were not sufficiently spread among the population of customers. This framework is about to be deployed in a commercial product for NTL detection.Comment: Proceedings of the 19th International Conference on Intelligent System Applications to Power Systems (ISAP 2017

Electric Non-Technical Losses

Ve své bakalářské práci se zabývám netechnickými ztrátami elektrické energie, a to převážně nelegálními odběry energie. Tuto problematiku probírám hlavně z právního hlediska a zabývám se výpočty množství neoprávněně odebrané energie a výpočty náhrady škody za tuto energii, tak jak je vykládají vyhlášky 82/2011 Sb. a 359/2020 Sb. Zároveň tímto porovnávám obě vyhlášky. Dále se v mé bakalářské práci zabývám provedením elektrických přípojek a dalšími náležitostmi s přípojkami spojenými, tak jak je stanovuje a určuje norma ČSN 33 3320 ed2. Hlavním výstupem této práce je výpočtový program v tabulkovém editoru EXCEL, pro výpočet množství neoprávněně odebrané elektrické energie a jeho následné nacenění.In my bachelor's thesis I deal with non-technical losses, mainly illegal energy consumption. I discuss this issue mainly from a legal point of view and I deal with the calculations of the amount of unauthorized energy taken and the calculations of compensation for this energy, as interpreted by Decree 82/2011 Coll. and 359/2020 Coll. At the same time, I am comparing the two decrees. Furthermore, in my bachelor's thesis I deal with the implementation of electrical connections and other essentials, as determined by the standard ČSN 33 3320 ed2. The main output of this work is a calculation program in the spreadsheet editor EXCEL, for calculating the amount of unauthorized electricity and its subsequent pricing.410 - Katedra elektroenergetikyvýborn

Semisupervised approach to non technical losses detection

Non-technical electrical losses detection is a complex task, with high economic impact. Due to the diversity and large number of consumption records, it is very important to find an efficient automatic method to detect the largest number of frauds with the least amount of experts hours involved in preprocessing and inspections. This article analyzes the performance of a strategy based on a semisupervised method, that starting from a set of labeled data, extends this labels to unlabeled data, and then allows to detect new frauds at consumptions. Results show that the proposed framework, improves performance in terms of the F measure against manual methods performed by experts and previous supervised methods, avoiding hours of experts/inspection labeling

Status of Non-Technical Losses of Electricity in Brazil

The electricity demand in Brazil has been growing. Some studies estimate that through 2035 the energy consumption (the power consumption) should increase 78%. Two distinct actions are necessary to meet this growth: the construction of new generating plants and to reduce electrical losses in the country. As the construction of power plants have a high price, coupled with the growth of (current) environmental concern, electric utilities are investing in reducing losses, both technical and non-technical. In this context, this paper aims to present an overview of nontechnical losses in Brazil and to raise a discussion on the reasons that contribute to energy fraud

SHAPE: The load prediction and non-technical losses modules

Data Analytics applied in the electric sector has been leveraged in the recent years, primarily owing to the introduction worldwide of Automatic Meter Reading/Management and smart grids technologies operated by electric Utilities. Utilities have to face with return of investment from this infrastructure and at the same time have to fulfil the promise of providing better and innovative service to the customer, retailer and distribution network. This paper deals with the innovative SHAPE Web software platform for Data Analytics applied to the load patterns sourced from the Italian Enel network’s smart meters. A previous contribution reported on the customer classification and segmentation modules implemented in the SHAPE platform. This work describes the Load prediction and Non-technical losses modules. The SHAPE Datawarehouse (DW) currently stores four years of progressively updated customer’s load patterns