Spatial–temporal model for wind speed in Lithuania

In this paper, we propose a spatial–temporal model for the wind speed (WS). We first estimate the model at the single spatial meteorological station independently on spatial correlations. The temporal model contains seasonality, a higher-order autoregressive component and a variance describing the remaining heteroskedesticity in residuals. We then model spatial dependencies by a Gaussian random field. The model is estimated on daily WS records from 18 meteorological stations in Lithuania. The validation procedure based on out-of-sample observations shows that the proposed model is reliable and can be used for various practical applications.

[1]  Richard E. Chandler,et al.  Inference for clustered data using the independence loglikelihood , 2007 .

[2]  Arthur Charpentier,et al.  Wind in Ireland: long memory or seasonal effect? , 2006 .

[3]  Shafiqur Rehman,et al.  Statistical characteristics of wind in Saudi Arabia , 1994 .

[4]  M. Milligan,et al.  Statistical Wind Power Forecasting Models: Results for U.S. Wind Farms; Preprint , 2003 .

[5]  J. Torres,et al.  Forecast of hourly average wind speed with ARMA models in Navarre (Spain) , 2005 .

[6]  V. Isham,et al.  An Analysis of Daily Maximum Wind Speed in Northwestern Europe Using Generalized Linear Models , 2002 .

[7]  Robert Haining,et al.  Statistics for spatial data: by Noel Cressie, 1991, John Wiley & Sons, New York, 900 p., ISBN 0-471-84336-9, US $89.95 , 1993 .

[8]  Fred Espen Benth,et al.  Analysis and modelling of wind speed in New York , 2010 .

[9]  L. V. Cremades,et al.  Analysis and modelling of time series of surface wind speed and direction , 1999 .

[10]  J. Torres,et al.  Fitting wind speed distributions: a case study , 1998 .

[11]  Pierre Ailliot,et al.  An autoregressive model with time‐varying coefficients for wind fields , 2006 .

[12]  Craig B. Borkowf,et al.  Time-Series Forecasting , 2002, Technometrics.

[13]  A. H. Murphy,et al.  Time Series Models to Simulate and Forecast Wind Speed and Wind Power , 1984 .

[14]  A. Brett,et al.  The Autocorrelation of Hourly Wind Speed Observations , 1991 .

[15]  J. C. Lam,et al.  A study of Weibull parameters using long-term wind observations , 2000 .

[16]  A. Celik.  A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey , 2004 .

[17]  J. Potthoff,et al.  Hedging of Spatial Temperature Risk with Market-Traded Futures , 2011 .

[18]  F. Benth,et al.  The volatility of temperature and pricing of weather derivatives , 2007 .

[19]  Fred Espen Benth,et al.  Stochastic Modelling of Temperature Variations with a View Towards Weather Derivatives , 2005 .

[20]  R. Corotis,et al.  Probability models of wind velocity magnitude and persistence , 1978 .

[21]  C. F. Ratto,et al.  Stochastic modelling of wind velocities time series , 1998 .

[22]  W. Dunsmuir,et al.  SPACE–TIME MODELLING OF SYDNEY HARBOUR WINDS , 2005 .

[23]  Fred Espen Benth,et al.  A Spatial-temporal Model for Temperature with Seasonal Variance , 2007 .

[24]  Mike Rees,et al.  5. Statistics for Spatial Data , 1993 .