León de Mora, CarlosBiscarri Triviño, FélixMonedero Goicoechea, Iñigo LuisGuerrero Alonso, Juan IgnacioBiscarri Triviño, JesúsMillán, Rocío2022-03-242022-03-242011León de Mora, C., Biscarri Triviño, F., Monedero Goicoechea, I.L., Guerrero Alonso, J.I., Biscarri Triviño, J. y Millán, R. (2011). Variability and Trend-Based Generalized Rule Induction Model to NTL Detection in Power Companies. IEEE Transactions on Power Systems, 26 (4), 1798-1807.0885-8950https://hdl.handle.net/11441/131240This 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.application/pdf10engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Customer electricity consumptionElectric fraudElectricity marketNon-technical lossesinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccesshttps://doi.org/10.1109/TPWRS.2011.2121350