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Delegates are invited to meet and discuss with the poster presenters during the poster presentation sessions between 10:30-11:30 and 16:00-17:00 on Thursday, 19 November 2015.

Lead Session Chair:
Stephan Barth, ForWind - Center for Wind Energy Research, Germany
José Vidal AWS Truepower, Spain
Santi Vila (1) F Jose Vidal (1) Michael Brower (1)
(1) AWS Truepower, Barcelona, Spain (2) AWS Truepower, Albany, United States of America

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Presenter's biography

Biographies are supplied directly by presenters at EWEA 2015 and are published here unedited

José started his graduate career as researcher and associate professor at the Meteorology Department in the University of Barcelona, in the field of Numerical Weather Prediction. Since 2003 he has been working in AWS Truepower in several applications of Numerical Methods to Wind Resource Assessment, including forecasting, wind mapping and energy assessment. Currently he is the Manager of the Consulting Services for Europe and Latin America.
He has been actively participating in several EU funded projects in diverse roles, including Work Package Leader. José is also member of TPWind Working Group in Resource Assessment.


Poster Download poster (13.73 MB)


Optimal use of mast information for resource assessment through uncertainty maps


Most wind energy projects employ more than one mast. This poses the practical challenge of combining the information from the various masts in estimating the energy production. One common approach is to divide the project area into sections, each of which is assigned to one mast; however, this may introduce discontinuities close to each section’s boundaries.
A smoother result can be obtained with the other industry standard method, which is the blending of the predicted wind resource from the different masts. The challenge is to determine a suitable method of weighting. A relatively simple blending technique is to weight each mast’s prediction according to the inverse of the squared distance to that mast.


Distance-weighted blending is relatively easy, but is not necessarily the best approach. It assumes implicitly that the uncertainty associated with the prediction from any given mast depends strictly on the distance to that mast. However, distance is only one factor influencing the accuracy of wind flow modeling.
In a previous work presented at the EWEA conference, the authors introduced the concept of uncertainty maps and the method to create them. The innovative approach here presented is the use of the uncertainty maps as the weights for blending the information from the different masts available in a project area.

Main body of abstract

None of the commonly used methods for combining the information from various masts is perfect. The division of the project area into sections, where one single mast “dominates” the area, can be defined by distance. However, depending of site characteristics, other kind of division criteria can be much more appealing, but also more subjective, as topographic similarity. A typical example of this can kind of division is the association of ridgetop sections with ridgetop masts. This approach is pragmatic, but it can be awkward when, as often happens, there is a discontinuity in the predicted wind resource where two sections meet. The resulting energy production estimate can change abruptly (and unrealistically) when a turbine is moved from one side of the dividing line to the other.
The blending method is more esthetically pleasing, but not necessarily more accurate. It adopts the assumption that every mast offers at least some useful information about the wind resource at any point, and that the weighted average of several estimates should be more reliable than any single estimate alone. But as mentioned before, the challenge is to determine a suitable method of weighting. Although distance plays for sure a role, other factors such as terrain slope and aspect, variations in land cover, and temperature gradients can also be important.
Statistical theory holds that if independent measurements of the same quantity are combined in a weighted average, where the weight accorded each measurement is inversely proportional to its uncertainty squared, the result of this combination has the lowest possible uncertainty. In previous studies, the concept and methodology of the uncertainty maps was developed, with direct applications in the design of measurement campaigns or in layout optimization. In the present work, we have used the uncertainty maps to optimally blend the information from the masts available.
The presentation will refresh the concept of the uncertainty maps and will show a comparison of the accuracies obtained using the different combination methods for a set of projects with an important number of met masts.


The optimum combination of mast data for wind resource assessment is still a challenging area for some projects. Uncertainty maps provide an objective and statistically optimum approach for blending the data with the highest achievable accuracy.

Learning objectives
The audience will be introduced to the combination of mast data methods, refresh the concept of uncertainty maps and see how can be used to optimally combine the available mast data.