Statistical wind direction modeling for the analysis of large scale wind power generation

Matti Koivisto*, Jussi Ekström, Ilkka Mellin, Robert Millar, Matti Lehtonen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)


Understanding the effects of large-scale wind power generation on the electric power system is growing in importance as the amount of installed generation increases. In addition to wind speed, the direction of the wind is important when considering wind farms, as the aggregate generation of the farm depends on the direction of the wind. This paper introduces the wrapped Gaussian vector autoregressive process for the statistical modeling of wind directions in multiple locations. The model is estimated using measured wind direction data from Finland. The presented methodology can be used to model new locations without wind direction measurements. This capability is tested with two locations that were left out of the estimation procedure. Through long-term Monte Carlo simulations, the methodology is used to analyze two large-scale wind power scenarios with different geographical distributions of installed generation. Wind generation data are simulated for each wind farm using wind direction and wind speed simulations and technical wind farm information. It is shown that, compared with only using wind speed data in simulations, the inclusion of simulated wind directions enables a more detailed analysis of the aggregate wind generation probability distribution.

Original languageEnglish
Pages (from-to)677-694
JournalWind Energy
Early online date25 Sept 2016
Publication statusPublished - 15 Mar 2017
MoE publication typeA1 Journal article-refereed


  • Circular correlation
  • Monte Carlo simulation
  • Vector autoregressive model
  • Wind direction
  • Wind power
  • Wrapped Gaussian process


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