Finding Wind Farms Quicker, Cheaper
Tuesday, July 21, 2015 @ 10:07 AM gHale
Through the use of artificial intelligence it may soon be possible to find the suitable location for a wind farm in as little as 3 months, compared to the usual 8 to 12 months.
The move is possible through a new statistical technique that yields better wind-speed predictions than existing techniques do — even when it uses only three months’ worth of data. That could save power companies time and money, particularly in the evaluation of sites for offshore wind farms, where maintaining measurement stations is particularly 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,” said Kalyan Veeramachaneni, a research scientist at Massachusetts Institute of Technology’s (MIT) Computer Science and Artificial Intelligence Laboratory (CSAIL) and first author of a paper on the subject. In particular, Veeramachaneni said, standard practice in the industry is to assume wind-speed data follows a Gaussian distribution, the bell curve familiar from basic statistics.
“The data here is non-Gaussian; we all know that,” Veeramachaneni said. “You can fit a bell curve to it, but that’s not an accurate representation of the data.”
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 a joint distribution which means 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 usually characterize that joint distribution as a Gaussian distribution, Veeramachaneni said.
Outside the Box
The first novelty of the model Veeramachaneni developed with his colleagues — Una-May O’Reilly, a principal research scientist at CSAIL, and Alfredo Cuesta-Infante of the Universidad Rey Juan Carlos in Madrid — is 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.
But its main advantage is it’s 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 nonparametric distributions, in which the data end up 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 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. But often, a curved line would 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 looking to install 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 major consultant in the wind industry.
With only 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 have improved their model by evaluating alternative ways of calculating joint distributions. According to additional analysis of the data from the Museum of Science, their revised approach could double the accuracy of their predictions.