Massachusetts Institute of Technology (MIT) researchers say they have developed a statistical technique that yields better wind-speed predictions than existing techniques. The researchers say their breakthrough could save power companies time and money, especially in the evaluation of sites for offshore wind farms, where maintaining measurement stations is particularly costly.
The first novelty of the technique is that it can factor in data from up to 15 or more weather stations, in some cases. In addition, it is not restricted to Gaussian probability distributions, also known as bell curves. The new model also can use different types of distributions to characterize data from different sites, and it can combine them in multiple ways. Another aspect of the new model is its ability to use nonparametric distributions, and to find nonlinear correlations between data sets.
The researchers applied the new model to data collected from an anemometer on top of the MIT Museum. The researchers used three months worth of wind data to predict wind speeds over the next two years three times as accurately as existing models could with eight months of data.
"This methodology has strong practical value, and I am convinced that it will be applicable to many other real-life problems," says Nanyang Technological University professor Justin Dauwels.
From MIT News
View Full Article
Abstracts Copyright © 2015 Information Inc., Bethesda, Maryland, USA
No entries found