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 language | English |
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Journal | Procedia Environmental Sciences |
Volume | 26 |
Pages (from-to) | 132-135 |
ISSN | 1878-0296 |
DOIs | |
Publication status | Published - 2015 |
Event | Spatial Statistics 2015: Emerging Patterns - Avignon, France Duration: 9 Jun 2015 → 12 Jun 2015 |
Conference
Conference | Spatial Statistics 2015 |
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Country/Territory | France |
City | Avignon |
Period | 09/06/2015 → 12/06/2015 |
Keywords
- Wind power prediction
- Bayesian hierarchical models
- integrated nested Laplace approximation