2018-07-052018-07-052006Monedero Goicoechea, I.L., Biscarri Triviño, F., León de Mora, C., Biscarri Triviño, J. y Millán, R. (2006). MIDAS: Detection of Non-technical Losses in Electrical Consumption Using Neural Networks and Statistical Techniques. En ICCSA 2006: International Conference on Computational Science and Its Applications (725-734), Glasgow, UK: Springer.978-3-540-34079-90302-9743https://hdl.handle.net/11441/76833Datamining 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.application/pdfengAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccess10.1007/11751649_806535522