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

Energies

Luis Hernandez¹  Carlos Baladrón²  Javier M. Aguiar²  Belén Carro²  Antonio J. Sanchez-Esguevillas² 

[1] Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Autovía de Navarra A15, salida 56, Lubia 42290, Soria, Spain; E-Mail:;Universidad de Valladolid, Escuela Técnica Superior de Ingenieros de Telecomunicación, Campus Miguel Delibes, Paseo de Belén 15, Valladolid 47011, Spain; E-Mails:

关键词: artificial neural network;    distributed intelligence;    short-term load forecasting;    smart grid;    microgrid;    multilayer perceptron;   

DOI  :  10.3390/en6031385

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.