@inproceedings{0b95002f0ef548b1951f31b061945f1d,
title = "Evaluation of Parametric Statistical Models for Wind Speed Probability Density Estimation",
abstract = "An accurate statistical estimation of wind speed probability density at a given site is crucial when making power network planning decisions involving wind generation resources. The use of parametric probability density functions, such as the Rayleigh, Weibull and Gaussian distributions, can be problematic as it can lead to model mis-specification at a given site. In this paper, the use of the Gaussian Mixture Model (GMM) to estimate wind speed variability is investigated and compared with the above three popular parametric models using wind speed data for six sites in northwest Europe. Results show that the GMM produces the lowest error values with the highest percentage improvements, and is the only model that consistently fails to reject the null hypothesis when conducting the K-S goodness-of-fit test.",
keywords = "Density estimation, Gaussian mixture model, statistical analysis, wind speed models",
author = "Maisam Wahbah and Omar Alhussein and El-Fouly, {Tarek H.M.} and Bashar Zahawi and Sami Muhaidat",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Electrical Power and Energy Conference, EPEC 2018 ; Conference date: 10-10-2018 Through 11-10-2018",
year = "2018",
month = dec,
day = "31",
doi = "10.1109/EPEC.2018.8598283",
language = "British English",
series = "2018 IEEE Electrical Power and Energy Conference, EPEC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 IEEE Electrical Power and Energy Conference, EPEC 2018",
address = "United States",
}
