Comparison of the performance of four measure–correlate–predict algorithms

Measure–correlate–predict (MCP) algorithms are used to predict the wind resource at target sites for wind power development. This paper describes some of the MCP approaches found in the literature and then compares the performance of four of them, using a common set of data from a variety of sites (complex terrain, coastal, offshore). The algorithms that are compared include a linear regression model, a model using distributions of ratios of the wind speeds at the two sites, a vector regression method, and a method based on the ratio of the standard deviations of the two data sets. The MCP algorithms are compared using a set of performance metrics that are consistent with the ultimate goals of the MCP process. The six different metrics characterize the estimation of (1) the correct mean wind speed, (2) the correct wind speed distribution, (3) the correct annual energy production at the target site, assuming a sample wind turbine power curve, and (4) the correct wind direction distribution. The results indicate that the method using the ratio of the standard deviations of the two data sets and the model that uses the distribution of ratios of the wind speeds at the two sites work the best. The linear regression model and the vector regression model give biased estimates of a number of the metrics, due to the characteristics of linear regression.