The most similar predictor – on selecting measurement locations for wind resource assessment

Abstract

We present the “most similar” method for selecting optimal measurement positions for wind resource assessment. 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. 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.