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Data bank: nine numerical methods for determining the parameters of weibull for wind energy generation tested by five statistical tools

International Journal of Power Electronics and Drive Systems · Vol. 12 No. 2 (2021) · pp. 1114–1130

DOI
10.11591/ijpeds.v12.i2.pp1114-1130
ISSN
2722-256X, 2088-8694
Published
2021-06-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

This study aims to determine the potential of wind energy in the mediterranean coastal plain of Palestine. The parameters of the Weibull distribution were calculated on basis of wind speed data. Accordingly, two approaches were employed: analysis of a set of actual time series data and theoretical Weibull probability function. In this analysis, the parameters Weibull shape factor ‘k’ and the Weibull scale factor ‘c’ were adopted. These suitability values were calculated using the following popular methods: method of moments (MM), standard deviation method (STDM), empirical method (EM), maximum likelihood method (MLM), modified maximum likelihood method (MMLM), second modified maximum likelihood method (SMMLM), graphical method (GM), least mean square method (LSM) and energy pattern factor method (EPF). The performance of these numerical methods was tested by root mean square error (RMSE), index of agreement (IA), Chi-square test (X2), mean absolute percentage error (MAPE) and relative root mean square error (RRMSE) to estimate the percentage of error. Among the prediction techniques. The EPF exhibited the greatest accuracy performance followed by MM and MLM, whereas the SMMLM exhibited the worst performance. The RMSE achieved the best prediction accuracy, whereas the RRMSE attained the worst prediction accuracy.

Keywords

Collaboration network of the authors

40 authors · 150 co-authorship links · 12 across different organisations · showing the 40 most connected

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  • International Islamic University Malaysia
  • International Islamic University, Islamabad
  • Özyeğin University
  • Universiti Teknologi MARA
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Nurul Fadzlin HasbullahInternational Islamic University, Islamabad5
Ahmed Samir Abed BadawiInternational Islamic University Malaysia5
Mohammed ElamassieÖzyeğin University5
Alhareth ZyoudInternational Islamic University Malaysia5
Majdee TohtayongAffiliation unknown4
Nur Shahida MidiAffiliation unknown4
Teddy Surya GunawanInternational Islamic University Malaysia3
Mashkuri YaacobAffiliation unknown3
Nurul Fadzlin HasbullahaAffiliation unknown3
Musse Mohamud AhmedAffiliation unknown2
Fawwaz WafaAffiliation unknown2
Ezzidin AboadlaAffiliation unknown2
Khairil Azhar AznanAffiliation unknown2
Aisha H HashimAffiliation unknown2
Mohamed Hadi HabaebiInternational Islamic University Malaysia1
Nur Syazana Izzati RazaliAffiliation unknown1
Saerahany Legori IbrahimAffiliation unknown1
Siti Nadiah Mohd SapihieAffiliation unknown1
Mira KartiwiInternational Islamic University Malaysia1
Agus PratondoAffiliation unknown1
Fatin Najihah Muhamad ZamriAffiliation unknown1
Yasir Mohd. MustafahAffiliation unknown1
Nofri Yenita DahlanUniversiti Teknologi MARA1
Adriana Haziqah Mohd AizamAffiliation unknown1
Saidatul Habsah AsmanAffiliation unknown1
S. M. A. MotakabberInternational Islamic University Malaysia1
Khadiza AkterAffiliation unknown1
A. H. M. Zahirul AlamAffiliation unknown1
Sajib AhmedAffiliation unknown1
Tania AnnurAffiliation unknown1
Suriza Ahmad ZabidiAffiliation unknown1
Muhamad Aqil Muqri Muhamad FahmiAffiliation unknown1
Mohd Shahrin Abu HanifahAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://ijpeds.iaescore.com/index.php/IJPEDS/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Data bank: nine numerical methods for determining the parameters of weibull for wind energy generation tested by five statistical tools</dc:title>
	<dc:creator>Badawi, Ahmed Samir</dc:creator>
	<dc:creator>Yusoff, Siti Hajar</dc:creator>
	<dc:creator>Zyoud, Alhareth Mohammed</dc:creator>
	<dc:creator>Khan, Sheroz</dc:creator>
	<dc:creator>Hashim, Aisha</dc:creator>
	<dc:creator>Uyaroğlu, Yılmaz</dc:creator>
	<dc:creator>Ismail, Mahmoud</dc:creator>
	<dc:subject xml:lang="en-US">Average wind speed; Cumulative distribution function; Probability distribution function; Numerical analysis; Statistical tools; Weibull parameters; Wind energy</dc:subject>
	<dc:description xml:lang="en-US">This study aims to determine the potential of wind energy in the mediterranean coastal plain of Palestine. The parameters of the Weibull distribution were calculated on basis of wind speed data. Accordingly, two approaches were employed: analysis of a set of actual time series data and theoretical Weibull probability function. In this analysis, the parameters Weibull shape factor ‘k’ and the Weibull scale factor ‘c’ were adopted. These suitability values were calculated using the following popular methods: method of moments (MM), standard deviation method (STDM), empirical method (EM), maximum likelihood method (MLM), modified maximum likelihood method (MMLM), second modified maximum likelihood method (SMMLM), graphical method (GM), least mean square method (LSM) and energy pattern factor method (EPF). The performance of these numerical methods was tested by root mean square error (RMSE), index of agreement (IA), Chi-square test (X2), mean absolute percentage error (MAPE) and relative root mean square error (RRMSE) to estimate the percentage of error. Among the prediction techniques. The EPF exhibited the greatest accuracy performance followed by MM and MLM, whereas the SMMLM exhibited the worst performance. The RMSE achieved the best prediction accuracy, whereas the RRMSE attained the worst prediction accuracy.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2021-06-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijpeds.iaescore.com/index.php/IJPEDS/article/view/21000</dc:identifier>
	<dc:identifier>10.11591/ijpeds.v12.i2.pp1114-1130</dc:identifier>
	<dc:source xml:lang="en-US">International Journal of Power Electronics and Drive Systems (IJPEDS); Vol 12, No 2: June 2021; 1114-1130</dc:source>
	<dc:source>2722-256X</dc:source>
	<dc:source>2088-8694</dc:source>
	<dc:source>10.11591/ijpeds.v12.i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijpeds.iaescore.com/index.php/IJPEDS/article/view/21000/13465</dc:relation>
	<dc:relation>https://ijpeds.iaescore.com/index.php/IJPEDS/article/downloadSuppFile/21000/2903</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Ahmed Samir Badawi, Siti Hajar Yusoff, Alhareth Mohammed Zyoud, Sheroz Khan, Aisha Hashim, Yılmaz Uyaroğlu, Mahmoud Ismail</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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