A generalized linear model approach to seasonal aspects of wind speed modeling

Abstract

The aim of the article is to identify the intraday seasonality in a wind speed time series. Following the traditional approach, the marginal probability law is Weibull and, consequently, we consider seasonal Weibull law. A new estimation and decision procedure to estimate the seasonal Weibull law intraday scale parameter is presented. We will also give statistical decision-making tools to discard or not the trend parameter and to validate the seasonal model.

Keywords:

• generalized linear models
• maximum likelihood estimation
• seasonality
• Weibull law
• wind speed time series

Acknowledgements

This research is partially supported by a grant from Électricité de France (EDF) and EDF Énergies Nouvelles (EEN). The first author acknowledges also support from the Research Grants Council of the Hong Kong Special Administrative Region (CityU 500111).

Notes

1. One can find more informations on this softwares respectively at https://analysis.nrel.gov/homer/ and http://www.wasp.dk/.

2. In fact, we can show that the data are not independent. There is a significative correlation before 48 h; after this period the wind speed can be considered as uncorrelated. For instance, autocorrelation coefficients on the data set of Section 6.2 are presented in Appendix 3. However, it is possible for our purpose to introduce two days spaces between observations (see [3]).