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Wednesday, 12 March 2014
11:15 - 12:45 Wind speed predictions: Are we at the limit of our knowledge or can we improve?
Resource Assessment  


Room: Ponent
Session description

No new long-term correction methods have appeared for years and it is possible that current techniques are optimal. There are several issues which affect any long-term correction analysis:

… from the mundane: the optimum reference period? how do we measure success? re-analysis data or ground based stations? non-integer years of data?

… to the exotic: atmospheric stability, climate change decadal variations, sun spots activity and solar cycles.

It is likely that long-term correction techniques which consider these may provide more reliable predictions than has previously been possible.

This session describes new long-term correction methodologies and compares the results with those of conventional methods. Innovative techniques to improve the representativeness of long-term data series are discussed, different long-term data series are compared and conclusions on the decadal-scale variability of the wind speed are presented. The overall objective of this session is to give insight on how these developments contribute to a greater certainty in future wind speed predictions.

Learning objectives

  • Evaluate innovative methods to improve the representativeness of long-term data series and the overall accuracy of long-term extrapolations
  • Compare new long-term correction methods to traditional methods
  • Understand how a more accurate description of the decadal-scale variability of the wind speed contributes to the reduction of the uncertainty in the long-term corrected wind speed
Lead Session Chair:
Sónia Liléo, Kjeller Vindteknikk AS, Sweden

Co-chair(s):
Steve Ross, 3Tier
Jesper N. Nissen Vattenfall Wind Power R&D, Denmark
Co-authors:
Jesper N Nissen (1) F P Sam Hawkins (1) Jens Madsen (1) Luca Delle Monache (4) Sue Ellen Haupt (4) Emilie Vanvyve (4)
(1) Vattenfall Wind Power - Research and development, Fredericia , Denmark (2) NCAR- Research Application Laboratory , Boulder , United States (3) National Center of Atmospheric Research , Boulder , United States (4) National Center for Atmospheric Research , Boulder , United States

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Presenter's biography

Biographies are supplied directly by presenters at EWEA 2014 and are published here unedited

Jesper N. Nissen has been working with wind power meteorology for more than 5 years. He is currently a senior R&D engineer at Vattenfall Wind Power R&D in Frederica Denmark - He studied meteorology and geophysics at University of Copenhagen, where he earned his Ph.D. in Coastal meteorology in 2006. After a postdoctoral employment at wind energy department, Risoe National Laboratory for sustainable energy (now DTU-Wind), he worked for Vestas Wind Systems before moving to Vattenfall Wind Power R&D. His main interests are resources assessment, short term predictions, boundary layer and wind power meteorology.

Abstract

ON THE USE OF ANALOG ENSEMBLES TO OBTAIN A LONG TERM WIND SPEED ESTIMATION AND QUANTIFYING UNCERTAINTY

Introduction

The wind resource and energy assessment is key to a wind farm development project and is based on a long-term estimate of the wind speed at a target site covering a period of typical 10 to 30-year. A new method is proposed and demonstrated for the long-term estimate of the wind speeds at several target sites, and benchmarked towards a traditional MCP method. This method is an ensemble based approach where the ensemble is made of analogs and therefore offers an inexpensive way to quantify uncertainty and accounting for advanced flow features such as wind-shear and stability.

Approach

The analog ensemble method for resource assessment can be described as follows. For each reanalysis time, the ensemble set of wind speed predictions is made from the set of observed wind speeds taken from a period where both reanalysis predictions and observations were available. These wind speed measurements are those concurrent to the past reanalysis that were the most similar to the current reanalysis. The similarity is based on a number of physical variables such as wind speed/direction, shear and bulk Richardson number among many others.

Main body of abstract

The analog ensemble is tested for several sites both onshore and offshore and the performance reported in traditional metrics such as correlation, rmse, bias, crmse and benchmarked towards a baseline MCP method. The probabilistic performance is evaluated in terms of reliability, spread skills, resolution and discrimination. A sensitivity analysis of the analog ensemble performance to key components of the algorithm is carried out, including on ensemble size, training length, analog predictor variables and driving reanalysis fields.
The analog-ensemble method is found to provide an estimation of the long-term wind speed that overall bears far better statistics than the starting reanalysis data set, and the MCP estimation of the long-term wind speed. However, the main advantage of the analog-ensemble method is that it yields a reliable estimate of the uncertainty associated with the reconstructed wind speed at each time step of the reconstructed period, as rigorously tested with a range of metrics.


Conclusion

An analog-ensemble method is demonstrated for use in wind farm development process.
• This method provides an accurate long-term wind resource estimate at target sites.
• It reliably quantifies the uncertainty allowing for cost-effective decision-making.
• The analog ensemble method is a computationally inexpensive method compared to other ensemble methods suitable for long term estimates of wind speed.
• The analog ensemble method is shown to have superior performance when compared to traditional MCP methods



Learning objectives
• Introduction to a novel method to probabilistic wind resource assessment and insight into how the new method compares to existing MCP resource assessment techniques. Benchmarking results from several targets sites with various complexities are reported.