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Título
Artificial neural networks for short-term load forecasting in microgrids environment
Autor
Año del Documento
2014
Editorial
Elsevier
Descripción
Producción Científica
Documento Fuente
Energy, October 2014, vol. 75, p. 252-264.
Resumen
The adaptation of energy production to demand has been traditionally very important for utilities in order to optimize resource consumption. This is especially true also in microgrids where many intelligent elements have to adapt their behaviour depending on the future generation and consumption conditions. However, traditional forecasting has been performed only for extremely large areas, such as nations and regions. This work aims at presenting a solution for short-term load forecasting (STLF) in microgrids, based on a three-stage architecture which starts with pattern recognition by a self-organizing map (SOM), a clustering of the previous partition via k-means algorithm, and finally demand forecasting for each cluster with a multilayer perceptron. Model validation was performed with data from a microgrid-sized environment provided by the Spanish company Iberdrola.
Materias Unesco
3306 Ingeniería y Tecnología Eléctricas
Palabras Clave
Artificial neural network
Short-term load forecasting
Microgrid
Pattern recognition
Self-organizing map
k-Means algorithm
ISSN
0360-5442
Revisión por pares
SI
Version del Editor
Propietario de los Derechos
© Elsevier
Idioma
eng
Tipo de versión
info:eu-repo/semantics/acceptedVersion
Derechos
openAccess
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