RT info:eu-repo/semantics/article
T1 Artificial neural networks for short-term load forecasting in microgrids environment
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 Short-term load forecasting
K1 Microgrid
K1 Pattern recognition
K1 Self-organizing map
K1 k-Means algorithm
K1 3306 Ingeniería y Tecnología Eléctricas
AB 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.
PB Elsevier
SN 0360-5442
YR 2014
FD 2014
LK https://uvadoc.uva.es/handle/10324/70424
UL https://uvadoc.uva.es/handle/10324/70424
LA eng
NO Energy, October 2014, vol. 75, p. 252-264.
NO Producción Científica
DS UVaDOC
RD 22-dic-2024