From lidar scans to roughness maps for wind resource modelling in forested areas

Applying erroneous roughness lengths can have a large impact on the estimated performance of wind turbines, particularly in forested areas. In this study, a new method called the objective roughness approach (ORA), which converts tree height maps created using airborne lidar scans to roughness ma...

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Main Authors: R. Floors, P. Enevoldsen, N. Davis, J. Arnqvist, E. Dellwik
Format: Article
Language:English
Published: Copernicus Publications 2018-06-01
Series:Wind Energy Science
Online Access:https://www.wind-energ-sci.net/3/353/2018/wes-3-353-2018.pdf
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spelling doaj-cb5de5d041c142d4a9d7ffa068ac36602020-11-24T21:04:48ZengCopernicus PublicationsWind Energy Science2366-74432366-74512018-06-01335337010.5194/wes-3-353-2018From lidar scans to roughness maps for wind resource modelling in forested areasR. Floors0P. Enevoldsen1P. Enevoldsen2N. Davis3J. Arnqvist4E. Dellwik5Department of Wind Energy, Technical University of Denmark, Roskilde, DenmarkCenter for Energy Technologies, Aarhus University, Aarhus, DenmarkEnvision Energy, Silkeborg, DenmarkDepartment of Wind Energy, Technical University of Denmark, Roskilde, DenmarkDepartment of Earth Sciences, Uppsala University, Uppsala, SwedenDepartment of Wind Energy, Technical University of Denmark, Roskilde, DenmarkApplying erroneous roughness lengths can have a large impact on the estimated performance of wind turbines, particularly in forested areas. In this study, a new method called the objective roughness approach (ORA), which converts tree height maps created using airborne lidar scans to roughness maps suitable for wind modelling, is evaluated via cross predictions among different anemometers at a complex forested site with seven tall meteorological masts using the Wind Atlas Analysis and Application Program (WAsP). The cross predictions were made using ORA maps created at four spatial resolutions and from four freely available roughness maps based on land use classifications. The validation showed that the use of ORA maps resulted in a closer agreement with observational data for all investigated resolutions compared to the land use maps. Further, when using the ORA maps, the risk of making large errors (> 25 %) in predicted power density was reduced by 40–50 % compared to satellite-based products with the same resolution. The results could be further improved for high-resolution ORA maps by adding the displacement height. The improvements when using the ORA maps were both due to a higher roughness length and due to the higher resolution.https://www.wind-energ-sci.net/3/353/2018/wes-3-353-2018.pdf
collection DOAJ
language English
format Article
sources DOAJ
author R. Floors
P. Enevoldsen
P. Enevoldsen
N. Davis
J. Arnqvist
E. Dellwik
spellingShingle R. Floors
P. Enevoldsen
P. Enevoldsen
N. Davis
J. Arnqvist
E. Dellwik
From lidar scans to roughness maps for wind resource modelling in forested areas
Wind Energy Science
author_facet R. Floors
P. Enevoldsen
P. Enevoldsen
N. Davis
J. Arnqvist
E. Dellwik
author_sort R. Floors
title From lidar scans to roughness maps for wind resource modelling in forested areas
title_short From lidar scans to roughness maps for wind resource modelling in forested areas
title_full From lidar scans to roughness maps for wind resource modelling in forested areas
title_fullStr From lidar scans to roughness maps for wind resource modelling in forested areas
title_full_unstemmed From lidar scans to roughness maps for wind resource modelling in forested areas
title_sort from lidar scans to roughness maps for wind resource modelling in forested areas
publisher Copernicus Publications
series Wind Energy Science
issn 2366-7443
2366-7451
publishDate 2018-06-01
description Applying erroneous roughness lengths can have a large impact on the estimated performance of wind turbines, particularly in forested areas. In this study, a new method called the objective roughness approach (ORA), which converts tree height maps created using airborne lidar scans to roughness maps suitable for wind modelling, is evaluated via cross predictions among different anemometers at a complex forested site with seven tall meteorological masts using the Wind Atlas Analysis and Application Program (WAsP). The cross predictions were made using ORA maps created at four spatial resolutions and from four freely available roughness maps based on land use classifications. The validation showed that the use of ORA maps resulted in a closer agreement with observational data for all investigated resolutions compared to the land use maps. Further, when using the ORA maps, the risk of making large errors (> 25 %) in predicted power density was reduced by 40–50 % compared to satellite-based products with the same resolution. The results could be further improved for high-resolution ORA maps by adding the displacement height. The improvements when using the ORA maps were both due to a higher roughness length and due to the higher resolution.
url https://www.wind-energ-sci.net/3/353/2018/wes-3-353-2018.pdf
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