The intermittence of the renewable sources due to its unpredictability increases the instability of the actual grid and energy supply. Besides, in a deregulated and competitive framework, producers and consumers require short-term forecasting tools to derive their bidding strategies to the electricity market. This paper proposes a novel hybrid computational tool, based on a combination of evolutionary particle swarm optimization with an adaptive-network-based fuzzy inference system, for wind power forecasting and electricity prices forecasting in the short-term. The results from two real-world case studies are presented, in order to illustrate the proficiency of the proposed computational tool. © 2012 IFIP International Federation for Information Processing.
CITATION STYLE
Osório, G. J., Pousinho, H. M. I., Matias, J. C. O., Monteiro, C., & Catalão, J. P. S. (2012). Hybrid evolutionary neuro-fuzzy computational tool to forecast wind power and electricity prices. In IFIP Advances in Information and Communication Technology (Vol. 372 AICT, pp. 321–328). Springer New York LLC. https://doi.org/10.1007/978-3-642-28255-3_35
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