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Energies 2015, 8(8), 8682-8703; doi:10.3390/en8088682

Wind Resource Mapping Using Landscape Roughness and Spatial Interpolation Methods

1
Environmental and Spatial Management, Faculty of Engineering and Architecture, Ghent University, Vrijdagmarkt 10-301, 9000 Ghent, Belgium
2
Institute of Physics, Carl von Ossietzky University, Ammerländer Heerstraße 136, 26129 Oldenburg, Germany
3
Power-Link, Ghent University, Wetenschapspark 1, 8400 Ostend, Belgium
*
Author to whom correspondence should be addressed.
Academic Editor: Vincenzo Dovì
Received: 13 May 2015 / Revised: 6 August 2015 / Accepted: 7 August 2015 / Published: 14 August 2015
(This article belongs to the Special Issue Energy Policy and Climate Change)
View Full-Text   |   Download PDF [6781 KB, uploaded 14 August 2015]   |  

Abstract

Energy saving, reduction of greenhouse gasses and increased use of renewables are key policies to achieve the European 2020 targets. In particular, distributed renewable energy sources, integrated with spatial planning, require novel methods to optimise supply and demand. In contrast with large scale wind turbines, small and medium wind turbines (SMWTs) have a less extensive impact on the use of space and the power system, nevertheless, a significant spatial footprint is still present and the need for good spatial planning is a necessity. To optimise the location of SMWTs, detailed knowledge of the spatial distribution of the average wind speed is essential, hence, in this article, wind measurements and roughness maps were used to create a reliable annual mean wind speed map of Flanders at 10 m above the Earth’s surface. Via roughness transformation, the surface wind speed measurements were converted into meso- and macroscale wind data. The data were further processed by using seven different spatial interpolation methods in order to develop regional wind resource maps. Based on statistical analysis, it was found that the transformation into mesoscale wind, in combination with Simple Kriging, was the most adequate method to create reliable maps for decision-making on optimal production sites for SMWTs in Flanders (Belgium). View Full-Text
Keywords: small and medium wind turbines; wind resource map; spatial interpolation; Simple Kriging; Flanders small and medium wind turbines; wind resource map; spatial interpolation; Simple Kriging; Flanders
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Van Ackere, S.; Van Eetvelde, G.; Schillebeeckx, D.; Papa, E.; Van Wyngene, K.; Vandevelde, L. Wind Resource Mapping Using Landscape Roughness and Spatial Interpolation Methods. Energies 2015, 8, 8682-8703.

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