---
title: "Copula Graphical Models for Wind Resource Estimation"
authors:
  - "Kalyan Veeramachaneni"
  - "Alfredo Cuesta-Infante"
  - "Una-May O'Reilly"
doi: ""
url: "https://ijcai.org/proceedings/2015/375"
pdf: "https://ijcai.org/proceedings/2015/0375.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "2646-2653"
track: "Special Track on Computational Sustainability"
keywords:
---

# Copula Graphical Models for Wind Resource Estimation

Kalyan Veeramachaneni, Alfredo Cuesta-Infante, Una-May O'Reilly

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, pages 2646-2653.

## Abstract

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.