Comparison of Low-Cost Resource Assessment Tools for Small-Scale Wind Energy Installations

Two promising approaches to low-cost wind resource assessment are compared in terms of their ability to predict the long-term wind resource at eight UK sites. The first approach is a semi-empirical model based on boundary layer scaling which requires no direct wind measurements at the site of interest. The second approach, named measure-correlate-predict (MCP), is a data-led led technique which increases the value of short-term wind data through correlation to a reference site. We extend the MCP approach by reducing the data collection period to just three months and compare the success of three different implementations of the technique. For the modelling approach, the percentage error in the predicted mean wind power density was 37% averaged across all eight sites. For the best performing MCP technique, this is reduced to approximately 12% after accounting for seasonal sensitivities. The results indicate that even very short-term measurements, when correlated to a reference site, can result in a large improvement in the accuracy of the predicted wind power density compared to a modelling approach.