Spatial models for probabilistic prediction of wind power with application to annual-average and high temporal resolution data

Amanda Lenzi, Pierre Pinson, Line H. Clemmensen, Gilles Guillot

Research output: Contribution to journalArticlepeer-review

Abstract

Producing accurate spatial predictions for wind power generation together with a quantification of uncertainties is required to plan and design optimal networks of wind farms. Toward this aim, we propose spatial models for predicting wind power generation at two different time scales: for annual average wind power generation, and for a high temporal resolution (typically wind power averages over 15-min time steps). In both cases, we use a spatial hierarchical statistical model in which spatial correlation is captured by a latent Gaussian field. We explore how such models can be handled with stochastic partial differential approximations of Matérn Gaussian fields together with Integrated Nested Laplace Approximations. We demonstrate the proposed methods on wind farm data from Western Denmark, and compare the results to those obtained with standard geostatistical methods. The results show that our method makes it possible to obtain fast and accurate predictions from posterior marginals for wind power generation. The proposed method is applicable in scientific areas as diverse as climatology, environmental sciences, earth sciences and epidemiology.
Original languageEnglish
Pages (from-to)1615-1631
JournalStochastic Environmental Research and Risk Assessment
Volume31
Early online date14 Oct 2016
DOIs
Publication statusPublished - 30 Sept 2017

Fingerprint

Dive into the research topics of 'Spatial models for probabilistic prediction of wind power with application to annual-average and high temporal resolution data'. Together they form a unique fingerprint.

Cite this