Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks

Luis Hernandez, Carlos Baladrón, Javier M. Aguiar, Belén Carro, Antonio J. Sanchez-Esguevillas and Jaime Lloret
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Luis Hernandez: Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Autovía de Navarra A15, salida 56, Lubia 42290, Soria, Spain
Carlos Baladrón: Universidad de Valladolid, Escuela Técnica Superior de Ingenieros de Telecomunicación, Campus Miguel Delibes, Paseo de Belén 15, Valladolid 47011, Spain
Javier M. Aguiar: Universidad de Valladolid, Escuela Técnica Superior de Ingenieros de Telecomunicación, Campus Miguel Delibes, Paseo de Belén 15, Valladolid 47011, Spain
Belén Carro: Universidad de Valladolid, Escuela Técnica Superior de Ingenieros de Telecomunicación, Campus Miguel Delibes, Paseo de Belén 15, Valladolid 47011, Spain
Antonio J. Sanchez-Esguevillas: Universidad de Valladolid, Escuela Técnica Superior de Ingenieros de Telecomunicación, Campus Miguel Delibes, Paseo de Belén 15, Valladolid 47011, Spain
Jaime Lloret: Universidad Politécnica de Valencia, Departamento de Comunicaciones, Camino Vera s/n. 46022, Valencia, Spain

Energies, 2013, vol. 6, issue 3, 1-24

Abstract: Electricity is indispensable and of strategic importance to national economies. Consequently, electric utilities make an effort to balance power generation and demand in order to offer a good service at a competitive price. For this purpose, these utilities need electric load forecasts to be as accurate as possible. However, electric load depends on many factors (day of the week, month of the year, etc. ), which makes load forecasting quite a complex process requiring something other than statistical methods. This study presents an electric load forecast architectural model based on an Artificial Neural Network ( ANN ) that performs Short-Term Load Forecasting ( STLF ). In this study, we present the excellent results obtained, and highlight the simplicity of the proposed model. Load forecasting was performed in a geographic location of the size of a potential microgrid , as microgrids appear to be the future of electric power supply.

Keywords: artificial neural network; distributed intelligence; short-term load forecasting; smart grid; microgrid; multilayer perceptron (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (25)

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