Proceedings Abstracts of the Twenty-Fourth International Joint Conference on Artificial Intelligence

Copula Graphical Models for Wind Resource Estimation / 2646
Kalyan Veeramachaneni, Alfredo Cuesta-Infante, Una-May O'Reilly

We develop multivariate copulas for modeling multiple joint distributions of wind speeds at a wind farm site and neighboring wind source. A ndimensional Gaussian copula and multiple copula graphical models enhance the quality of the prediction site distribution. The models, in comparison to multiple regression, achieve higher accuracy and lower cost because they require less sensing data.