Energies (Mar 2013)

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

  • Antonio J. Sanchez-Esguevillas,
  • Jaime Lloret,
  • Belén Carro,
  • Javier M. Aguiar,
  • Luis Hernandez,
  • Carlos Baladrón

DOI
https://doi.org/10.3390/en6031385
Journal volume & issue
Vol. 6, no. 3
pp. 1385 – 1408

Abstract

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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.

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