@article{Lizondo_2015,
	title = {Análisis de {Variables} {Temporales} para la {Predicción} del {Consumo} {Eléctrico}},
	issn = {1390-5074},
	url = {http://www.sebastianrodriguez.com.ar/files/Lizondo_et_al_2015_Análisis_de_Variables_Temporales_para_la_Predicción_del_Consumo_Eléctrico.pdf},
	abstract = {Short Term Load Forecasting (STLF) currently is a major importance issue for Energy Companies. STFL allows a more efficient manage and use of resources and equipment. The electric demand prediction is a complex issue, since it depends or is related to economic factors, climate and time to mention a few. Furthermore, its behaviour changes from one society to another. Each factor provides a particular variable that could be presented in different forms, particularly the time variables. In this paper we present the hypothesis that the way an input variable is introduced to an energy prediction system affects the result. To validate this hypothesis, different methods to represent time variables were considered and applied to the prediction problem of daily electric consumption in Tucumán, a province of Argentina. The separation of the time variables into single variables representing the day, day of the week, month and year for each period involved into the problem, was the most convenient method. The improvement of this method was about the 10 \% in comparison to the others.},
	number = {11},
	journal = {Revista Técnica Enerǵıa},
	author = {Lizondo, Diego and Jimenez, Victor A. and Villacis Postigo, Fernando and Will, Adrian and Rodriguez, Sebastian},
	month = jan,
	year = {2015},
	keywords = {Linear regresion, energy consumption, short-term load forecasting, variable selection},
	pages = {5--12},
}

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