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

  • Íñigo Monedero
  • Félix Biscarri
  • Carlos León
  • Jesús Biscarri
  • Rocío Millán
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3984)

Abstract

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.

Keywords

Neural Network Electrical Consumption Electrical Company Private User Fraud Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Íñigo Monedero
    • 1
  • Félix Biscarri
    • 1
  • Carlos León
    • 1
  • Jesús Biscarri
    • 2
  • Rocío Millán
    • 2
  1. 1.Departamento de Tecnología ElectrónicaEscuela Técnica Superior de Ingeniería InformáticaSevilleSpain
  2. 2.EndesaSevilleSpain

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