Modeling Wind Speed Using Parametric and Non-Parametric Distribution Functions

Abstract

The variability of wind speed is modeled in the literature using probability distribution functions (PDFs). In many papers, the selection of PDFs is based solely on goodness-of-fit metrics without giving proper consideration to whether these PDFs can model the wind speed range. A PDF’s ability to model the wind speed range ensures that it can synthesize both the minimum and the maximum wind speed at a site. A methodology to select PDFs that can be used to model wind speed is presented in this paper. The proposed methodology considers not only the goodness-of-fit metrics when selecting PDFs, but also their ability to model the wind speed range. The proposed methodology is compared with a commonly used methodology in the literature that selects PDFs based solely on goodness-of-fit metrics. Simulation results show that the proposed methodology chooses better PDFs than the commonly used methodology. Furthermore, it is shown that selecting PDFs using the commonly used methodology does not guarantee that the chosen PDFs will model the wind speed range.

Keywords

• Goodness-of-fit metrics
• kernel density estimation
• logspline density estimation
• wind speed modeling
• wind speed range