Stochastic simulation of hourly average wind speed sequences in Tangiers (Morocco)

Wind Derivatives: Modeling and Pricing

“…The data were collected from Brown et al 1984;Daniel and Chen 1991;Garcia et al 1998;Justus et al 1978;Kavak Akpinar and Akpinar 2005;Nfaoui et al 1996;Torres et al 2005; J. r. a. Benth and Benth 2010;Celik 2004;Tuller and Brett 1984).…”

“…However the majority of the studies utilize variations of the general ARMA model, (Ailliot et al 2006;Billinton et al 1996;Brett and Tuller 1991;Daniel and Chen 1991;Huang and Chalabi 1995;Kamal and Jafri 1997;Lei et al 2009;Nfaoui et al 1996;Rehman and Halawani 1994;Torres et al 2005). In this paper we will first estimate the dynamics of the detrended and deseasonalized DAWSs process using a general ARMA model and then we will compare our results with a WN.…”

Hourly Average Wind Speed Simulation and Forecast Based on ARMA Model in Jeju Island, Korea

“…These papers proposed the general approach for modeling wind speed [1] and case study of wind speed statistical models in specific site or multiple sites [3,4,6,[10][11][12]. The wind speed data were almost transformed from Weibull distribution to normal distribution by Dubey method which the shape parameters of data is transformed to 3.6 and the power m for the transformation was specified by measure of distribution symmetry [1], Skewness method [3,4,6,10] or by Box-Cox method which the power l for the transformation is specified by minimizing the Skewness of the data [8].…”

“…These papers proposed the general approach for modeling wind speed [1] and case study of wind speed statistical models in specific site or multiple sites [3,4,6,[10][11][12]. The wind speed data were almost transformed from Weibull distribution to normal distribution by Dubey method which the shape parameters of data is transformed to 3.6 and the power m for the transformation was specified by measure of distribution symmetry [1], Skewness method [3,4,6,10] or by Box-Cox method which the power l for the transformation is specified by minimizing the Skewness of the data [8]. However, the transformed data was not checked the strongly stationary [1,10] or instead of checking the strongly stationary of transformed data, normal distribution is only considered by comparing with corresponding normal probability density [3,4,8] or testing non-stationary of transformed data by Duckey-fuller test [9].…”

“…The wind speed data were almost transformed from Weibull distribution to normal distribution by Dubey method which the shape parameters of data is transformed to 3.6 and the power m for the transformation was specified by measure of distribution symmetry [1], Skewness method [3,4,6,10] or by Box-Cox method which the power l for the transformation is specified by minimizing the Skewness of the data [8]. However, the transformed data was not checked the strongly stationary [1,10] or instead of checking the strongly stationary of transformed data, normal distribution is only considered by comparing with corresponding normal probability density [3,4,8] or testing non-stationary of transformed data by Duckey-fuller test [9]. These papers assumed that transformed data fitted to a p th order autoregressive process AR(p) [1] or the transformed data fitted to a p th order autoregressive process AR(p) by autocorrelation, partial autocorrelation analysis in identification process but there was no using Bayesian information criterion (BIC) to select an accurate class of ARMA(p,q) [3] or ACF, PACF and BIC were analyzed but the AR(p) was selected too soon after ACF and PACF analysis [8], instead of analyzing ACF, PACF and BIC together or using only AIC [9,10].…”