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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2018-06-01
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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 |
work_keys_str_mv |
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