%0 Journal Article %1 Haslett1989SpaceTime %A Haslett, John %A Raftery, Adrian E. %D 1989 %J Journal of the Royal Statistical Society. Series C (Applied Statistics) %K wind statistics %N 1 %P 1--50 %R 10.2307/2347679 %T Space-Time Modelling with Long-Memory Dependence: Assessing Ireland's Wind Power Resource %U http://www.jstor.org/stable/2347679 %V 38 %X We consider estimation of the long term average power output from a wind turbine generator at a site for which few data on wind speeds are available. Long term records of wind speeds at the 12 synoptic meteorological stations are also used. Inference is based on a simple and parsimonious approximating model which accounts for the main features of wind speeds in Ireland, namely seasonal effects, spatial correlation, short-memory temporal autocorrelation and long-memory temporal dependence. It synthesizes deseasonalization, kriging, ARMA modelling and fractional differencing in a natural way. A simple kriging estimator performs well as a point estimator, and good interval estimators result from the model. The resulting procedure is easy to apply in practice.

@article{Haslett1989SpaceTime, abstract = {We consider estimation of the long term average power output from a wind turbine generator at a site for which few data on wind speeds are available. Long term records of wind speeds at the 12 synoptic meteorological stations are also used. Inference is based on a simple and parsimonious approximating model which accounts for the main features of wind speeds in Ireland, namely seasonal effects, spatial correlation, short-memory temporal autocorrelation and long-memory temporal dependence. It synthesizes deseasonalization, kriging, ARMA modelling and fractional differencing in a natural way. A simple kriging estimator performs well as a point estimator, and good interval estimators result from the model. The resulting procedure is easy to apply in practice.}, added-at = {2018-06-18T21:23:34.000+0200}, author = {Haslett, John and Raftery, Adrian E.}, biburl = {https://www.bibsonomy.org/bibtex/2643cbc96953a53792db616bb8477e2ec/pbett}, citeulike-article-id = {10859008}, citeulike-linkout-0 = {http://dx.doi.org/10.2307/2347679}, citeulike-linkout-1 = {http://www.jstor.org/stable/2347679}, citeulike-linkout-2 = {http://www.jstor.org/stable/info/2347679#bibInfo}, doi = {10.2307/2347679}, interhash = {0bf1d4a8d1762d29448391ec4c785238}, intrahash = {643cbc96953a53792db616bb8477e2ec}, issn = {00350035-9254-9254}, journal = {Journal of the Royal Statistical Society. Series C (Applied Statistics)}, keywords = {wind statistics}, number = 1, pages = {1--50}, posted-at = {2012-07-05 11:01:33}, priority = {2}, timestamp = {2018-06-22T18:32:06.000+0200}, title = {Space-Time Modelling with Long-Memory Dependence: Assessing Ireland's Wind Power Resource}, url = {http://www.jstor.org/stable/2347679}, volume = 38, year = 1989 }