New model predicts wind speeds more accurately with three
months of data than others do with 12.
(July 17, 2015) When
a power company wants to build a new wind farm, it generally hires a consultant
to make wind speed measurements at the proposed site for eight to 12 months.
Those measurements are correlated with historical data and used to assess the
site’s power-generation capacity.
At the International Joint Conference on Artificial
Intelligence later this month, MIT researchers will present 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,” says Kalyan Veeramachaneni, a research scientist at MIT’s
Computer Science and Artificial Intelligence Laboratory (CSAIL) and first
author on the new paper. In particular, Veeramachaneni says, standard practice
in the industry is to model correlations in wind-speed data using a so-called
Gaussian distribution — the “bell curve” familiar from basic statistics.
“The data here is non-Gaussian; we all know that,”
Veeramachaneni says. “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.