This study makes use of data from six meteorological masts to examine five different data sources of roughness lengths. The data sources can be applied in most regions of the world. The consequences of applying wrong roughness lengths can impact the business case of a wind project. The experiment confirmed the preliminary expectation, as the optimized roughness approach provided better results than the remaining four approaches and, furthermore, was able to treat different tree heights. The initial test was conducted using a spatial resolution of 20 m for optimized roughness approach, while the other data sources used a greater resolution. As a response, optimized roughness approach was reused for the other spatial resolutions showing better results than the remaining approaches. One other remarkable finding associated with this study was the relationship between spatial resolution and errors in the estimation, as a resolution above 100 m provided random results with no relationship whatsoever.
EnevoldsenP (2016a) Onshore wind energy in Northern European forests: Reviewing the risks. Renewable and Sustainable Energy Reviews60: 1251–1262.
9.
EnevoldsenP (2016b) Wind Power in Forested Areas: Determining the Roughness Length and Displacement Height for Coniferous Trees. New Orleans, LA: AWEA.
10.
EnevoldsenP (2017) Managing the Risks of Wind Farms in Forested Areas: Design Principles for Northern Europe (1st edn). Aarhus: Aarhus University.
11.
EnevoldsenPMeissnerCJothiprakasamV (2017) Validation of CFD Forest Simulations based on a Global Forest Parameter Database. Amsterdam: WindEurope.
12.
EnevoldsenPPermienF-H (2018) Mapping the wind energy potential of Sweden: A sociotechnical wind Atlas. Journal of Renewable Energy2018: 1650794.
13.
EnevoldsenPValentineSV (2016) Do onshore and offshore wind farm development patterns differ?Energy for Sustainable Development35: 41–51.
14.
EnevoldsenPValentineSVSovacoolBK (2018). Insights into wind sites: Critically assessing the innovation, cost, and performance dynamics of global wind energy development. Energy policy120: 1–7.
FrerisLL1990. Wind Energy Conversion System (WECSY). Prentice Hall, UK.
17.
FloorsREnevoldsenPDavisN, et al. (2018) From lidar scans to roughness maps for wind resource modelling in forested areas. Wind Energy Science3(1): 353–370.
18.
GarrattJ (1992) The Atmospheric Boundary Layer. Cambridge: Cambridge University Press.
19.
Global Land Cover Facility (2016) Available at: http://www.landcover.org/ (accessed 8 October 2016).
20.
GreenhalghTPeacockR (2005) Effectiveness and efficiency of search methods in systematic reviews of complex evidence: Audit of primary sources. BMJ331: 1064–1065.
21.
HicksBHysonPMooreC (1975) A study of Eddy Fluxes over a forest. Journal of Applied Meteorology14: 58–66.
22.
JarvisPGJamesGBLandsbergJJ (1976) Coniferous forest. Vegetation and the Atmosphere2: 171–240.
23.
LeeI-HLeeY-HHongJ (2011) Characteristics of canopy turbulence over a deciduous forest on complex terrain. Asia-Pacific Journal of Atmospheric Science47: 281–291.
24.
RaupachMThomA (1981) Turbulence in and above plant canopies. Annual Review of Fluid Mechanics13: 97–129.
25.
SahinA (2004) Progress and recent trends in wind energy. Progress in Energy and Combustion Science30: 501–543.
Van EstR (1999) Winds of Change: A Comparative Study of the Politics of Wind Energy Innovation in California and Denmark. Amsterdam: International Books.
29.
VenäläinenAZengHPeltolaH, et al. (2004) Simulations of the influence of forest management on wind climate on a regional scale. Agricultural and Forest Meteorology123: 149–158.
WenzelAKalthoffNHorlacherV (1997) On the profiles of wind velocity in the roughness sublayer above a coniferous forest. Boundary Layer Meteorology84: 219–230.
