A Spatial Model for the Instantaneous Estimation of Wind Power at a Large Number of Unobserved Sites

Amanda Lenzi, Gilles Guillot, Pierre Pinson

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Abstract

We propose a hierarchical Bayesian spatial model to obtain predictive densities of wind power at a set of un-monitored locations. The model consists of a mixture of Gamma density for the non-zero values and degenerated distributions at zero. The spatial dependence is described through a common Gaussian random field with a Matérn covariance. For inference and prediction, we use the GMRF-SPDE approximation implemented in the R-INLA package. We showcase the method outlined here on data for 336 wind farms located in Denmark. We test the predictions derived from our method with model-diagnostic tools and show that it is calibrated.
Original languageEnglish
JournalProcedia Environmental Sciences
Volume26
Pages (from-to)132-135
ISSN1878-0296
DOIs
Publication statusPublished - 2015
EventSpatial Statistics 2015: Emerging Patterns - Avignon, France
Duration: 9 Jun 201512 Jun 2015

Conference

ConferenceSpatial Statistics 2015
CountryFrance
CityAvignon
Period09/06/201512/06/2015

Keywords

  • Wind power prediction
  • Bayesian hierarchical models
  • integrated nested Laplace approximation

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