Weekes, SM and Tomlin, AS (2014) Data efficient measure-correlate-predict approaches to wind resource assessment for small-scale wind energy. Renewable Energy, 63. 162 - 171. ISSN 0960-1481
Abstract
The feasibility of predicting the long-term wind resource at 22 UK sites using a measure-correlate-predict (MCP) approach based on just three months onsite wind speed measurements has been investigated. Three regression based techniques were compared in terms of their ability to predict the wind resource at a target site based on measurements at a nearby reference site. The accuracy of the predicted parameters of mean wind speed, mean wind power density, standard deviation of wind speeds and the Weibull shape factor was assessed, and their associated error distributions were investigated, using long-term measurements recorded over a period of 10 years. For each site, 120 wind resource predictions covering the entire data period were obtained using a sliding window approach to account for inter-annual and seasonal variations. Both the magnitude and sign of the prediction errors were found to be strongly dependent on the season used for onsite measurements. Averaged across 22 sites and all seasons, the best performing MCP approach resulted in mean absolute and percentage errors in the mean wind speed of 0.21ms and 4.8% respectively, and in the mean wind power density of 11Wm and 14%. The average errors were reduced to 3.6% in the mean wind speed and 10% in the mean wind power density when using the optimum season for onsite wind measurements. These values were shown to be a large improvement on the predictions obtained using an established semi-empirical model based on boundary layer scaling. The results indicate that the MCP approaches applied to very short onsite measurement periods have the potential to be a valuable addition to the wind resource assessment toolkit for small-scale wind developers.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2013, Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Renewable Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Renewable Energy, [VOL 63, (2014)] DOI 10.1016/j.renene.2013.08.033 |
Keywords: | Wind resource assessment; measure-correlate-predict; small-scale wind energy |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) > Energy Research Institute (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 17 Mar 2014 13:32 |
Last Modified: | 15 Sep 2014 01:44 |
Published Version: | http://dx.doi.org/10.1016/j.renene.2013.08.033 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.renene.2013.08.033 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:78143 |