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Hybrid Evolutionary Neuro-fuzzy Computational Tool to Forecast Wind Power and Electricity Prices

Abstract : 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.
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G. Osório, H. Pousinho, J. Matias, C. Monteiro, J. Catalão. Hybrid Evolutionary Neuro-fuzzy Computational Tool to Forecast Wind Power and Electricity Prices. 3rd Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS), Feb 2012, Costa de Caparica, Portugal. pp.321-328, ⟨10.1007/978-3-642-28255-3_35⟩. ⟨hal-01365601⟩

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