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

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 anal ysis, in the Spanish Power Electric Industry. It is divided into four section: data selection, data preprocessing, descriptive, and pre dictive 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 consump tion and offers a predictive model, based on the Generalized Rule Induction (GRI) model. This predictive analysis discovers associa tion 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.