A method based on the Van der Hoven spectrum for performance evaluation in prediction of wind speed

Authors : Elif KAYA, Burak BARUTÇU, Şükran Sibel MENTEŞ

Pages : 681-689

Doi:10.3906/tar-1210-90

View : 22 | Download : 6

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :Development of techniques for accurate assessment of wind power potential at a site is very important for the planning and establishment of a wind energy system. The most important defining character of the wind and the problems related with it lie in its unpredictable variation. Van der Hoven constructed a wind speed spectrum using short-term and long-term records of wind in Brookhaven, NY, USA, in 1957 and showed the diurnal and turbulent effects. His spectrum suggests that there is a substantial amount of wind energy in 1-min periodic variations. The aim of this paper is to evaluate the results of wind predictions using linear and nonlinear methods following the construction of power spectra insert ignore into journalissuearticles values(Van der Hoven spectrum); based on airport wind data in İstanbul. In this study, we have constructed power spectra of surface wind speed in order to evaluate the contributions of disturbances at various scales on the total spectrum. For this purpose, data from an automatic weather observation system at Atatürk Airport in İstanbul at a height of 10 m with a sampling rate of 1 min from 2005 to 2009 were used. In the second part of the study, autoregressive insert ignore into journalissuearticles values(AR); and artificial neural network insert ignore into journalissuearticles values(ANN); models were applied for prediction of wind speed. The prediction methods were assessed by comparing the characteristic frequency components of the prediction series and the real series. The best results were obtained from the ANN model; however, the AR model was found to moderately show the spectral characteristics.
Keywords : Key words Van der Hoven spectrum, autoregressive model, artificial neural networks, time series prediction