The most similar predictor – on selecting measurement locations for wind resource assessment
<p>We present the “most similar” method for selecting optimal measurement positions for wind resource assessment.</p> <p>Wind resource assessment is generally done by extrapolating a measured and long-term corrected wind climate at one location to a prediction location using a...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-12-01
|
| Series: | Wind Energy Science |
| Online Access: | https://wes.copernicus.org/articles/5/1679/2020/wes-5-1679-2020.pdf |
| Summary: | <p>We present the “most similar” method for selecting optimal measurement positions for wind resource assessment.</p>
<p>Wind resource assessment is generally done by extrapolating a measured and long-term corrected wind climate at one location to a prediction location using a micro-scale flow model. If several measurement locations are available, standard industry practice is to make a weighted average of all the possible predictions using inverse-distance weighting. The most similar method challenges this practice. Instead of weighting several predictions, the method only selects the single measurement location evaluated to be most similar.</p>
<p>We validate the new approach by comparing against measurements from 185 met masts from 40 wind farm sites and show improvements compared to inverse-distance weighting. Compared to using the closest measurement location, the error of power density predictions is reduced by 13 % using inverse-distance weighting and 34 % using the most similar method.</p> |
|---|---|
| ISSN: | 2366-7443 2366-7451 |