Fast Numerical Wind Turbine Candidate Site Evaluation

Abstract

A long-term measured wind speed time series from the location is typically used when deciding on placing a small wind turbine at a particular location. These data take a long time to collect. The presented novel method of measuring for a shorter time, using the measurement data for training an experimental model, and predicting the wind in a longer time period enables one to avoid most of the wait for the data collection. As the model inputs, the available long-term signals that consist of measurements from the meteorological stations in the vicinity and numerical weather predictions are used. Various possible experimental modelling methods that are based on linear or nonlinear regression models are tested in the field sites. The study area is continental with complex terrain, hilly topography, diverse land use, and no prevailing wind. It is shown that the method gives good results, showing linear regression is most advantageous, and that it is easy enough to use to be practically applicable in small wind projects of limited budget. The method is better suited to small turbines than to big ones because the turbines sited at low heights and in areas with low average wind speeds, where numerical weather prediction models are less accurate, tend to be small.