@inbook{5798ec6fa5df45b7a81db68b6b3345e2,
title = "Space-time trajectories of wind power generation: Parameterized precision matrices under a Gaussian copula approach",
abstract = "Emphasis is placed on generating space-time trajectories of wind power generation, consisting of paths sampled from high-dimensional joint predictive densities, describing wind power generation at a number of contiguous locations and successive lead times. A modelling approach taking advantage of the sparsity of precision matrices is introduced for the description of the underlying space-time dependence structure. The proposed parametrization of the dependence structure accounts for important process characteristics such as lead-time-dependent conditional precisions and direction-dependent cross-correlations. Estimation is performed in a maximum likelihood framework. Based on a test case application in Denmark, with spatial dependencies over 15 areas and temporal ones for 43 hourly lead times (hence, for a dimension of n = 645), it is shown that accounting for space-time effects is crucial for generating skilful trajectories.",
author = "Julija Tastu and Pierre Pinson and Henrik Madsen",
year = "2015",
doi = "10.1007/978-3-319-18732-7_14",
language = "English",
isbn = "978-3-319-18731-0",
series = "Lecture Notes in Statistics",
publisher = "Springer",
number = "217",
pages = "267--296",
editor = "Anestis Antoniadis and { Poggi}, Jean-Michel and Xavier Brossat",
booktitle = "Modeling and Stochastic Learning for Forecasting in High Dimensions",
note = "International Workshop on Industry Practices for Forecasting ; Conference date: 05-06-2013 Through 07-06-2013",
}