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

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 ave...

Full description

Saved in:

Bibliographic Details
Published in:	Stochastic environmental research and risk assessment Vol. 31; no. 7; pp. 1615 - 1631
Main Authors:	Lenzi, Amanda, Pinson, Pierre, Clemmensen, Line H., Guillot, Gilles
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01-09-2017 Springer Nature B.V
Subjects:	Approximation Aquatic Pollution Chemistry and Earth Sciences Climatology Computational Intelligence Computer Science Data processing Earth and Environmental Science Earth Sciences Electric power generation Environment Environmental science Epidemiology Geostatistics Math. Appl. in Environmental Science Mathematical models Original Paper Physics Predictions Probability Theory and Stochastic Processes Statistical analysis Statistical models Statistics for Engineering Stochasticity Temporal resolution Waste Water Technology Water Management Water Pollution Control Wind farms Wind power Wind power generation Spatial prediction Wind power Latent Gaussian field Integrated nested Laplace approximation
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!