RT info:eu-repo/semantics/article
T1 Short-term load forecasting for microgrids based on artificial neural networks
A1 Hernández Callejo, Luis
A1 Baladrón García, Carlos
A1 Aguiar Pérez, Javier Manuel
A1 Carro Martínez, Belén
A1 Sánchez Esguevillas, Antonio Javier
A1 Lloret, Jaime
K1 Artificial neural network
K1 Distributed intelligence
K1 Short-term electric load forecasting
K1 Smart grid
K1 Microgrid
K1 Multilayer perceptron
K1 33 Ciencias Tecnológicas
AB 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.
PB MDPI
YR 2013
FD 2013
LK https://uvadoc.uva.es/handle/10324/57244
UL https://uvadoc.uva.es/handle/10324/57244
LA eng
NO Energies, 2013, vol. 6, n. 3, p. 1385-1408
NO Producción Científica
DS UVaDOC
RD 25-dic-2024