Siting Wind Farms More Quickly and Less ExpensivelyEngineering360 News Desk | July 21, 2015
Researchers from the Massachusetts Institute of Technology (MIT) have developed a statistical technique that yields better wind-speed predictions than existing techniques, even when it uses limited amounts of data. The technique could save power companies time and money, particularly in evaluating sites for offshore wind farms, where maintaining measurement stations can be costly.
"We talked with people in the wind industry, and we found that they were using a very, very simplistic mechanism to estimate the wind resource at a site," says Kalyan Veeramachaneni, a research scientist at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). In particular, Veeramachaneni says, standard practice in the industry is to assume that wind-speed data follows a so-called Gaussian distribution—the "bell curve" familiar from basic statistics.
Typically, a wind energy consultant will find correlations between wind speed measurements at a proposed site and those made during the same period at a nearby weather station where records stretch back for decades. On the basis of those correlations, the consultant will adjust the weather station's historical data to provide an approximation of wind speeds at the new site.
The correlation model is what is known in statistics as a “joint distribution.” That means that it represents the probability not only of a particular measurement at one site, but of that measurement's coincidence with a particular measurement at the other. Wind-industry consultants, Veeramachaneni says, usually characterize that joint distribution as a Gaussian distribution.
The first novelty of the model that Veeramachaneni has developed with his colleagues is that it can factor in data from more than one weather station. In some of their analyses, the researchers used data from 15 or more other sites.
However, its main advantage is that it is not restricted to Gaussian probability distributions. Moreover, it can use different types of distributions to characterize data from different sites, and it can combine them in different ways. It can even use so-called nonparametric distributions, in which the data are described not by a mathematical function, but by a collection of samples, much the way a digital music file consists of discrete samples of a continuous sound wave.
Another aspect of the model is that it can find nonlinear correlations between data sets. Standard regression analysis, of the type commonly used in the wind industry, identifies the straight line that best approximates a scattering of data points, according to some distance measure. However, a curved line would often offer a better approximation. The researchers' model allows for that possibility.
The researchers first applied their technique to data collected from an anemometer on top of the MIT Museum, which was considering installing a wind turbine on its roof. Once they had evidence of their model's accuracy, they applied it to data provided to them by a wind industry consultant.
With three months of the company's historical data for a particular wind farm site, Veeramachaneni and his colleagues were able to predict wind speeds over the next two years three times as accurately as existing models could with eight months of data. Since then, the researchers say they have improved their model by evaluating alternative ways of calculating joint distributions.