Assessing distributions for monthly mean wind speed data

Mira Syahirah Kamil, Ahmad Mahir Razali

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Analysis of the wind speed behavior will contribute the vital information for the wind energy potential and its development. Hence, this study focuses on fitting several distributions to determine the most appropriate probability distribution that will describe the wind pattern in Kuala Terengganu and Mersing. Four different statistical distributions have been fitted to the monthly mean wind speed from eight different directions. Two stations of Kuala Terengganu and Mersing have been observed for the period 2000 to 2008. These distributions were tested using Kolmogorov-Smirnov statistic to find the best fit for describing the observed data. The Weibull distribution shows a clear fit for all wind speed directions in both locations.

Original languageEnglish
Title of host publication2016 UKM FST Postgraduate Colloquium: Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2016 Postgraduate Colloquium
PublisherAmerican Institute of Physics Inc.
Volume1784
ISBN (Electronic)9780735414464
DOIs
Publication statusPublished - 17 Nov 2016
Event2016 Postgraduate Colloquium of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology, UKM FST 2016 - Selangor, Malaysia
Duration: 13 Apr 201614 Apr 2016

Other

Other2016 Postgraduate Colloquium of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology, UKM FST 2016
CountryMalaysia
CitySelangor
Period13/4/1614/4/16

Fingerprint

windpower utilization
statistical distributions
stations
statistics

Keywords

  • goodness of fit
  • probability distributions
  • wind speed

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Kamil, M. S., & Razali, A. M. (2016). Assessing distributions for monthly mean wind speed data. In 2016 UKM FST Postgraduate Colloquium: Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2016 Postgraduate Colloquium (Vol. 1784). [050011] American Institute of Physics Inc.. https://doi.org/10.1063/1.4966830

Assessing distributions for monthly mean wind speed data. / Kamil, Mira Syahirah; Razali, Ahmad Mahir.

2016 UKM FST Postgraduate Colloquium: Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2016 Postgraduate Colloquium. Vol. 1784 American Institute of Physics Inc., 2016. 050011.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kamil, MS & Razali, AM 2016, Assessing distributions for monthly mean wind speed data. in 2016 UKM FST Postgraduate Colloquium: Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2016 Postgraduate Colloquium. vol. 1784, 050011, American Institute of Physics Inc., 2016 Postgraduate Colloquium of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology, UKM FST 2016, Selangor, Malaysia, 13/4/16. https://doi.org/10.1063/1.4966830
Kamil MS, Razali AM. Assessing distributions for monthly mean wind speed data. In 2016 UKM FST Postgraduate Colloquium: Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2016 Postgraduate Colloquium. Vol. 1784. American Institute of Physics Inc. 2016. 050011 https://doi.org/10.1063/1.4966830
Kamil, Mira Syahirah ; Razali, Ahmad Mahir. / Assessing distributions for monthly mean wind speed data. 2016 UKM FST Postgraduate Colloquium: Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2016 Postgraduate Colloquium. Vol. 1784 American Institute of Physics Inc., 2016.
@inproceedings{45572bc55f10488890ab99ee14f8932b,
title = "Assessing distributions for monthly mean wind speed data",
abstract = "Analysis of the wind speed behavior will contribute the vital information for the wind energy potential and its development. Hence, this study focuses on fitting several distributions to determine the most appropriate probability distribution that will describe the wind pattern in Kuala Terengganu and Mersing. Four different statistical distributions have been fitted to the monthly mean wind speed from eight different directions. Two stations of Kuala Terengganu and Mersing have been observed for the period 2000 to 2008. These distributions were tested using Kolmogorov-Smirnov statistic to find the best fit for describing the observed data. The Weibull distribution shows a clear fit for all wind speed directions in both locations.",
keywords = "goodness of fit, probability distributions, wind speed",
author = "Kamil, {Mira Syahirah} and Razali, {Ahmad Mahir}",
year = "2016",
month = "11",
day = "17",
doi = "10.1063/1.4966830",
language = "English",
volume = "1784",
booktitle = "2016 UKM FST Postgraduate Colloquium: Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2016 Postgraduate Colloquium",
publisher = "American Institute of Physics Inc.",

}

TY - GEN

T1 - Assessing distributions for monthly mean wind speed data

AU - Kamil, Mira Syahirah

AU - Razali, Ahmad Mahir

PY - 2016/11/17

Y1 - 2016/11/17

N2 - Analysis of the wind speed behavior will contribute the vital information for the wind energy potential and its development. Hence, this study focuses on fitting several distributions to determine the most appropriate probability distribution that will describe the wind pattern in Kuala Terengganu and Mersing. Four different statistical distributions have been fitted to the monthly mean wind speed from eight different directions. Two stations of Kuala Terengganu and Mersing have been observed for the period 2000 to 2008. These distributions were tested using Kolmogorov-Smirnov statistic to find the best fit for describing the observed data. The Weibull distribution shows a clear fit for all wind speed directions in both locations.

AB - Analysis of the wind speed behavior will contribute the vital information for the wind energy potential and its development. Hence, this study focuses on fitting several distributions to determine the most appropriate probability distribution that will describe the wind pattern in Kuala Terengganu and Mersing. Four different statistical distributions have been fitted to the monthly mean wind speed from eight different directions. Two stations of Kuala Terengganu and Mersing have been observed for the period 2000 to 2008. These distributions were tested using Kolmogorov-Smirnov statistic to find the best fit for describing the observed data. The Weibull distribution shows a clear fit for all wind speed directions in both locations.

KW - goodness of fit

KW - probability distributions

KW - wind speed

UR - http://www.scopus.com/inward/record.url?scp=85014668329&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85014668329&partnerID=8YFLogxK

U2 - 10.1063/1.4966830

DO - 10.1063/1.4966830

M3 - Conference contribution

AN - SCOPUS:85014668329

VL - 1784

BT - 2016 UKM FST Postgraduate Colloquium: Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2016 Postgraduate Colloquium

PB - American Institute of Physics Inc.

ER -