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An Artificial Neural Network Approach for Short-Term Wind Power Forecasting in Portugal

Name: An Artificial Neural Network Approach for Short-Term Wind Power Forecasting in Portugal
Keywords: Artificial neural networks, Models, Wind power, Speed, Prediction, Forecasting

descriptionPublicationkeyboard_double_arrow_right Conference object , Article 01 Nov 2009 Portugal Publisher:IEEEJournal:2009 15th International Conference on Intelligent System Applications to Power Systems

Authors: Catalão, João Paulo da Silva; Pousinho, Hugo Miguel Inácio; Mendes, Victor;

doi: 10.1109/isap.2009.5352853

handle: 10400.21/1272

An Artificial Neural Network Approach for Short-Term Wind Power Forecasting in Portugal

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Abstract

This paper presents an artificial neural network approach for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. The accuracy of the wind power forecasting attained with the proposed approach is evaluated against persistence and ARIMA approaches, reporting the numerical results from a real-world case study.

Country

Portugal

Related Organizations

Instituto Politécnico de Lisboa
Portugal
University of Beira Interior
Portugal
Instituto Politécnico de Lisboa
Portugal

Keywords

Artificial neural networks, Models, Wind power, Speed, Prediction, Forecasting

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	91
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 1%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 1%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%