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

Improving Knowledge-Based Systems with statistical techniques, text mining, and neural networks for non-technical loss detection

Currently, power distribution companies have several problems that are related to energy losses. For example, the energy used might not be billed due to illegal manipulation or a breakdown in the customer’s measurement equipment. These types of losses are called non-technical losses (NTLs), and these losses are usually greater than the losses that are due to the distribution infrastructure (technical losses). Traditionally, a large number of studies have used data mining to detect NTLs, but to the best of our knowledge, there are no studies that involve the use of a Knowledge-Based System (KBS) that is created based on the knowledge and expertise of the inspectors. In the present study, a KBS was built that is based on the knowledge and expertise of the inspectors and that uses text mining, neural networks, and statistical techniques for the detection of NTLs. Text mining, neural networks, and statistical techniques were used to extract information from samples, and this information was translated into rules, which were joined to the rules that were generated by the knowledge of the inspectors. This system was tested with real samples that were extracted from Endesa databases. Endesa is one of the most important distribution companies in Spain, and it plays an important role in international markets in both Europe and South America, having more than 73 million customers

A data mining method based on the variability of the customer consumption. A special application on electric utility companies

This paper describes a method proposed in order to recover electrical energy (lost by abnormality or fraud) by means of a data mining analysis based in outliers detection. It provides a general methodology to obtain a list of abnormal users using only the general customer databases as input. The hole input information needed is taken exclusively from the general customers’ database. The data mining method has been successfully applied to detect abnormalities and fraudulencies in customer consumption. We provide a real study and we include a number of abnormal pattern examples

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

Increasing the efficiency in non-technical losses detection in utility companies

Usually, the fraud detection method in utility companies uses the consumption information, the economic activity, the geographic location, the active/reactive ration and the contracted power. This paper proposes a combined text mining and neural networks to increase the efficiency in NonTechnical Losses (NTLs) detection methods which was previously applied. This proposed framework proposes to collect all the information that normally cannot be treated with traditional methods. This framework is part of a research project. This project is done in collaboration with Endesa, one of the most important power distribution companies of Europe. Currently, the proposed framework is in the test stage and it uses real cases

A real application on non-technical losses detection: the MIDAS Project

The MIDAS project began at 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’ facilities. 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 test phase and it is applied in real cases in company databases

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

Integration of the Computer Simulation and the Traditional Teaching. A Continuous Classroom Assessment

La Universidad de Berkeley desarrolló el programa PSPICE para simulación de circuitos electrónicos. De hecho, el acrónimo PSPICE hace referencia a ‘Programa de Simulación centrado en la Integración de Circuitos’. El sujeto principal de este artículo es presentar el esfuerzo de un grupo de profesores universitarios en innovación docente: el desarrollo de un material educativo semipresencial que aúna PSPICE y la enseñanza de la electrónica de Potencia. Se ha aplicado este material a una evaluación continua del alumnado.U.C. Berkeley developed the SPICE program to simulate integrated circuits. In fact, the acronym SPICE stands for Simulation Program with Integrated Circuit Emphasis. The aim of this paper is to present a team effort in education innovation: the development of an education material to join the SPICE program to simulate integrated circuits teaching and Power Electronics teaching. A continuous classroom assessment has been developed

Evaluación activa y mejora de la calidad de enseñanza: metodologías e indicadores

En este trabajo se presentan losresultados de una actividad innovadora que ha consistido en la realización de pruebas o ejercicios semanales, que han sido valorados, corregidos y devueltos al alumnado con las anotaciones y recomendacionessuficientes como para evitar, en un futuro, las deficiencias cometidas. Se hace una descripción de la metodología utilizada y cómo, con la información recogida,se pueden generar una serie de indicadores que permiten valorarla evolución general de la clase y establecer una predicción de losresultadosfinales. La experiencia innovadora no ha sido positiva, porque ha supuesto un incremento importante de la dedicación docente, sin que ésta se haya visto recompensada por una mejora en los resultados académicos generales (número de aprobados y porcentaje de presentados), aunque sí se ha percibido cierto aumento en la nota media.In this work, new educational activity results are shown. Each week an exam in classroom is made Evaluation and recommendations of corrected exams are returned to studentsto avoid mistakesin future. This paper describesthe used methodology and how several indicators, that show the global evolution of students and can do a prediction of final academic results, are obtained from collected information. At the end, the activity didn't generate the expected results: there was no improvement in global parameters (passed and presented ratios) although the mean calification of passed students wasslighty higherthan the last year