 Comparison between ANNs and linear MCP algorithms in the long-term estimation of the cost per kWh produced by a wind turbine at a candidate site: A case study in the Canary IslandsSergio Velázquez, José A. Carta and J.M. Matías Applied Energy, 2011, vol. 88, issue 11, 3869-3881 Abstract: In the work presented in this paper Artificial Neural Networks (ANNs) were used to estimate the long-term wind speeds at a candidate site. The specific costs of the wind energy were subsequently determined on the basis of the knowledge of these wind speeds. The results were compared with those obtained with a linear Measure–Correlate–Predict (MCP) method. The mean hourly wind speeds and directions recorded over a 10year period at six weather stations located on different islands in the Canary Archipelago (Spain) were used as a case study. The power-wind speed curves for five wind turbines of different rated power were also used. The mean absolute percentage error (MAPE), Pearson’s correlation coefficient and the Index of Agreement (IoA) between measured and estimated data were used to evaluate the errors made with the different metrics analysed. Keywords: Artificial Neural Network; Long-term estimation; Measure correlate predict; Variance Ratio Method; Wind energy cost (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:88:y:2011:i:11:p:3869-3881 Ordering information: This journal article can be ordered from DOI: 10.1016/j.apenergy.2011.05.007 Access Statistics for this article Applied Energy is currently edited by J. Yan More articles in Applied Energy from Elsevier |
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