Wind Resource Mapping Using Landscape Roughness and Spatial Interpolation Methods

Samuel Van Ackere, Greet Van Eetvelde, David Schillebeeckx, Enrica Papa, Karel Van Wyngene and Lieven Vandevelde
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Samuel Van Ackere: Environmental and Spatial Management, Faculty of Engineering and Architecture, Ghent University, Vrijdagmarkt 10-301, 9000 Ghent, Belgium
Greet Van Eetvelde: Environmental and Spatial Management, Faculty of Engineering and Architecture, Ghent University, Vrijdagmarkt 10-301, 9000 Ghent, Belgium
David Schillebeeckx: Institute of Physics, Carl von Ossietzky University, Ammerländer Heerstraße 136, 26129 Oldenburg, Germany
Karel Van Wyngene: Power-Link, Ghent University, Wetenschapspark 1, 8400 Ostend, Belgium
Lieven Vandevelde: Power-Link, Ghent University, Wetenschapspark 1, 8400 Ostend, Belgium

Energies, 2015, vol. 8, issue 8, 1-22

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).

Keywords: small and medium wind turbines; wind resource map; spatial interpolation; Simple Kriging; Flanders (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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