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A Study of Wind Statistics Through Auto-Regressive and Moving-Average (ARMA) Modeling
作者姓名:John  Z.YIM
作者单位:Department of Harbour and River Engineering,National Taiwan Ocean University,Department of Harbour and River Engineering,National Taiwan Ocean University Keelung 20224,China,Keelung 20224,China
摘    要:Statistical properties of winds near the Taichung Harbour are investigated. The 26 years'incomplete data of wind speeds, measured on an hourly basis, are used as reference. The possibility of imputation using simulated results of the Auto-Regressive (AR), Moving-Average (MA), and/ or Auto-Regressive and Moving-Average (ARMA) models is studied. Predictions of the 25-year extreme wind speeds based upon the augmented data are compared with the original series. Based upon the results, predictions of the 50- and 100-year extreme wind speeds are then made.


A Study of Wind Statistics Through Auto-Regressive and Moving-Average (ARMA) Modeling
John Z. YIM and Chun-Ren CHOU.A Study of Wind Statistics Through Auto-Regressive and Moving-Average (ARMA) Modeling[J].China Ocean Engineering,2001(1):61-72.
Authors:John Z YIM and Chun-Ren CHOU
Institution:Department of Harbour and River Engineering, National Taiwan Ocean University, Keelung 20224, China Department of Harbour and River Engineering, National Taiwan Ocean University, Keelung 20224, China
Abstract:Statistical properties of winds near the Taichung Harbour are investigated. The 26 years'incomplete data of wind speeds, measured on an hourly basis, are used as reference. The possibility of imputation using simulated results of the Auto-Regressive (AR), Moving-Average (MA), and/ or Auto-Regressive and Moving-Average (ARMA) models is studied. Predictions of the 25-year extreme wind speeds based upon the augmented data are compared with the original series. Based upon the results, predictions of the 50- and 100-year extreme wind speeds are then made.
Keywords:Auto-Regressive and Moving-Average (ARM A) modeling  probability distributions  extreme wind speeds
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