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Title: Comparison between the short-term observed and long-term estimated wind power density using artificial neural networks. A case study
Authors: Velázquez, S. 
Carta, J. A. 
UNESCO Clasification: 3303 ingeniería y tecnología químicas
3307 Tecnología electrónica
Keywords: Artificial Neural Network
Wind Farm
Wind Power
Wind Speed
Issue Date: 2011
Journal: Renewable energy and power quality journal 
Abstract: The economic feasibility of a wind project is dependent on the wind regime since it relies on the power output of the turbines over the installation’s working life. Consequently, the interannual variability of wind speed at a potential wind energy conversion site is an issue of capital importance. Usually a wind data measurement campaign is limited to a period no longer than one year (i.e. short-term). Therefore, the process of decision-making for wind farm constructors must be based in this short-term data. Various methods have been proposed in the scientific literature for estimation of the long-term wind speed characteristics at such sites. These methods use simultaneous measurements of the wind speed at the site in question and at one or several nearby reference sites with a long history of wind data measurements. In this paper, long-term wind power densities which have been estimated through the use Artificial Neural Networks (ANNs), will be compared to those which have been calculated by means of the short-term wind data (i.e. considered to be representative of long-term wind performance). Mean hourly wind speeds and directions calculated in a 10 year period of time at six weather stations located on six different islands in the Canarian Archipelago (Spain) were used in this study. Among the different conclusions which this study revealed, we can highlight that the wind resource estimation based on ANNs is better than that dependant on short-term wind data. This is true when the correlation coefficient between the reference and candidate weather station is of 0.6.
ISSN: 2172-038X
DOI: 10.24084/repqj09.595
Source: Renewable Energy and Power Quality Journal [ISSN 2172-038X],v. 1 (9), p. 1203-1208
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Comparison between the short-term observed and long-term estimated wind power density using artificial neural networks. A case study
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