Artificial neural networks for short-term load forecasting in microgrids environment

Luis Hernández, Carlos Baladrón, Javier M. Aguiar, Belén Carro, Antonio Sánchez-Esguevillas and Jaime Lloret

Energy, 2014, vol. 75, issue C, 252-264

Abstract: 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.

Keywords: Artificial neural network; Short-term load forecasting; Microgrid; Pattern recognition; Self-organizing map; k-Means algorithm (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (49)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544214008871
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:75:y:2014:i:c:p:252-264

DOI: 10.1016/j.energy.2014.07.065

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().