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Variability and Trend-Based Generalized Rule Induction Model to NTL Detection in Power Companies

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6 Author(s)
Leon, C. ; Univ. of Seville, Seville, Spain ; Biscarri, F. ; Monedero, I. ; Guerrero, J.I.
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This paper proposes a comprehensive framework to detect non-technical losses (NTLs) and recover electrical energy (lost by abnormalities or fraud) by means of a data mining analysis, in the Spanish Power Electric Industry. It is divided into four section: data selection, data preprocessing, descriptive, and predictive data mining. The authors insist on the importance of the knowledge of the particular characteristics of the Power Company customer: the main features available in databases are described. The paper presents two innovative statistical estimators to attach importance to variability and trend analysis of electric consumption and offers a predictive model, based on the Generalized Rule Induction (GRI) model. This predictive analysis discovers association rules in the data and it is supplemented by a binary Quest tree classification method. The quality of this framework is illustrated by a case study considering a real database, supplied by Endesa Company.

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Power Systems, IEEE Transactions on  (Volume:26 ,  Issue: 4 )