Comparison of feature selection methods using ANNs in MCP-wind speed methods. A case study

José A. Carta, Pedro Cabrera, José M. Matías and Fernando Castellano

Applied Energy, 2015, vol. 158, issue C, 490-507

Abstract: Recent studies in the field of renewable energies, and specifically in wind resource prediction, have shown growing interest in proposals for Measure–Correlate–Predict (MCP) methods which simultaneously use data recorded at various reference weather stations. In this context, the use of a high number of reference stations may result in overspecification with its associated negative effects. These include, amongst others, an increase in the estimation error and/or overfitting which could be detrimental to the generalisation capacity of the model when handling new data (prediction).

Keywords: Measure–correlate–predict method; Artificial neural networks; Wind speed; Wind direction; Feature selection; Cross-validation technique (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (16)

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DOI: 10.1016/j.apenergy.2015.08.102

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