Comparing Different Labeling Strategies in Anomalous Power Consumptions Detection

  • Fernanda Rodríguez
  • Federico Lecumberry
  • Alicia Fernández
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9443)


Detecting anomalous events is a complex task, specially when it should be performed manually and for several hours. In the case of electrical power consumptions, the detection of non-technical losses also has a high economic impact. The diversity and big number of consumption records, makes it very important to find an efficient automatic method for detecting the largest number of frauds. This work analyses the performance of a strategy based on learning from expert labeling: suspect/no-suspect, with one using inspection labels: fraud/no-fraud. Results show that the proposed framework, suitable for imbalance problems, improves performance in terms of the \(F_{measure}\) with inspection labels, avoiding hours of experts labeling.


Electricity fraud Support vector machine Optimum Path Forest Unbalance class problem Combining classifier UTE 



This work was supported by the program Sector Productivo CSIC UTE. Authors would like to thank UTE, especially Juan Pablo Kosut and Fernando Santomauro, for providing datasets and share fraud detection expertise.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Fernanda Rodríguez
    • 1
  • Federico Lecumberry
    • 1
  • Alicia Fernández
    • 1
  1. 1.Facultad de Ingeniería, Instituto de Ingeniería EléctricaUniversidad de la RepúblicaMontevideoUruguay

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