Forecasting of the electric energy demand trend and monthly fluctuation with neural networks

Authors:
Eva González-Romera;Miguel Ángel Jaramillo-Morán;Diego Carmona-Fernández
Affiliations:
Escuela de Ingenierías Industriales, Universidad de Extremadura, Avda de Elvas s/n, 06071 Badajoz, Spain;Escuela de Ingenierías Industriales, Universidad de Extremadura, Avda de Elvas s/n, 06071 Badajoz, Spain;Escuela de Ingenierías Industriales, Universidad de Extremadura, Avda de Elvas s/n, 06071 Badajoz, Spain
Venue:
Computers and Industrial Engineering
Year:
2007

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Abstract

Electric energy demand forecasting is a fundamental tool for production and distribution companies because it provides them with a prediction of the market demand of electric energy. Two kinds of forecasting may be performed: short term and medium to long term. This work is focused on monthly prediction, which is useful for the maintenance planning of grids and as market research for electricity producers and resellers. The timed series of monthly electric energy demands presents a rising tendency due to the influence of economic and technological evolution on the electric market. Embedded in this general trend is a fluctuation caused by the difference in demand from month to month. This paper proposes the extraction of that trend to perform separate predictions of both tendency and fluctuation with neural networks, which will be summed up to obtain the series forecasting. A Mean Absolute Percentage Error (MAPE) of about 2% has been obtained.