Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/57244
Título
Short-term load forecasting for microgrids based on artificial neural networks
Autor
Año del Documento
2013
Editorial
MDPI
Descripción
Producción Científica
Documento Fuente
Energies, 2013, vol. 6, n. 3, p. 1385-1408
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.
Materias Unesco
33 Ciencias Tecnológicas
Palabras Clave
Artificial neural network
Distributed intelligence
Short-term electric load forecasting
Smart grid
Microgrid
Multilayer perceptron
Revisión por pares
SI
Version del Editor
Propietario de los Derechos
© 2013 The Author(s)
Idioma
eng
Tipo de versión
info:eu-repo/semantics/publishedVersion
Derechos
openAccess
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