Open AccessModelling spatially and temporally correlated wind speed time series over a large geographical area using VARMA
Author(s) -
Yunus Kalid,
Chen Peiyuan,
Thiringer Torbjörn
Publication year - 2017
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2016.0235
Subject(s) - wind speed , gaussian , series (stratigraphy) , time series , moving average , variance (accounting) , standard deviation , autoregressive–moving average model , wind direction , autoregressive model , meteorology , mathematics , statistics , computer science , geology , geography , physics , paleontology , accounting , quantum mechanics , business
This study presents a modified vector auto‐regressive moving average (VARMA) modelling procedure to model spatially and temporally correlated wind speed time series over wide geographical areas. The standard VARMA is normally used to model stationary time series with Gaussian distribution. However, wind speed is non‐stationary (mean and variance varies over time) and non‐Gaussian. Hence, a method that can be used to transform wind speed data into a stationary and Gaussian time series is introduced in the modified procedure. To show the applicability of the procedure for different scenarios, six cases are investigated in the North and the Baltic Sea. The results show that the procedure can be used to model spatially and temporally correlated wind speed over a large geographical area. In addition, the resulting model can capture probability distribution and periodic characteristics of the wind speed data. Furthermore, based on the investigated case, it is shown that a vector auto‐regressive model of order three is a reasonable model structure which can be used to model spatially and temporally correlated wind speed in the North and the Baltic Sea area provided that the power transformed wind speed data is normalised by its monthly mean value and its variance.