This is an article we recently published on "Renewable and Sustainable Energy Reviews". It starts with a thorough review of the methods used for wind resource assessment: from algorithms based on physical laws to other based on statistics, plus mixed methods.
In the second part of the manuscript we present a method for wind resource assessment based on the application of Random Forest, coded completely in R.
Elsevier allows to download the full paper for FREE until the 12th of February, so if you are interested please download a copy.
This is the link:
http://authors.elsevier.com/a/1SG5a4s9HvhNZ6
Below is the abstract.
Abstract
Wind resource assessment is fundamental when selecting a site for wind energy projects. Wind is influenced by several environmental factors and understanding its spatial variability is key in determining the economic viability of a site. Numerical wind flow models, which solve physical equations that govern air flows, are the industry standard for wind resource assessment. These methods have been proven over the years to be able to estimate the wind resource with a relatively high accuracy. However, measuring stations, which provide the starting data for every wind estimation, are often located at some distance from each other, in some cases tens of kilometres or more. This adds an unavoidable amount of uncertainty to the estimations, which can be difficult and time consuming to calculate with numerical wind flow models. For this reason, even though there are ways of computing the overall error of the estimations, methods based on physics fail to provide planners with detailed spatial representations of the uncertainty pattern. In this paper we introduce a statistical method for estimating the wind resource, based on statistical learning. In particular, we present an approach based on ensembles of regression trees, to estimate the wind speed and direction distributions continuously over the United Kingdom (UK), and provide planners with a detailed account of the spatial pattern of the wind map uncertainty.
