Multi-Modal Wind Speed Modeling Using Mixture Probability Density Functions

Mahzan Dalawir; Mohammad Borooshan; Ahmed Azab; Maher Azzouz; Ahmed S.A. Awad

doi:10.1109/ITC-Egypt55520.2022.9855681

Multi-Modal Wind Speed Modeling Using Mixture Probability Density Functions

Mahzan Dalawir^*
, Mohammad Borooshan
, Ahmed Azab
, Maher Azzouz^*
, Ahmed S.A. Awad^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

For stochastic planning and operation of modern wind power farms, probability distribution functions (PDFs) are developed to estimate wind speed probabilities in a more predictable fashion. The parameters of the proposed model are obtained using the expectation-maximization (EM) algorithm to determine the model maximum log log-likelihood estimation (MLE). Additionally, the Bayesian information criterion (BIC), and Bootstrap Likelihood ratio test techniques are employed to analyze the appropriate number of PDFs and to determine the goodness of fit, respectively. The results demonstrate the accuracy of the proposed multi-modal PDFs compared to that of the well-known single PDFs utilized in the literature.

Original language	English
Title of host publication	International Telecommunications Conference, ITC-Egypt 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665488082
DOIs	https://doi.org/10.1109/ITC-Egypt55520.2022.9855681
State	Published - 2022
Externally published	Yes

Publication series

Name	International Telecommunications Conference, ITC-Egypt 2022 - Proceedings

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Bayesian information criterion (BIC)
Expectation-maximization algorithm (EM)
Maximum log-likelihood estimation (MLE)
Multi-modal wind speed modeling
mixture PDFs
probability distribution function (PDF)

ASJC Scopus subject areas

Computer Networks and Communications
Signal Processing
Instrumentation

Access to Document

10.1109/ITC-Egypt55520.2022.9855681

Cite this

Dalawir, M., Borooshan, M., Azab, A., Azzouz, M., & Awad, A. S. A. (2022). Multi-Modal Wind Speed Modeling Using Mixture Probability Density Functions. In International Telecommunications Conference, ITC-Egypt 2022 - Proceedings (International Telecommunications Conference, ITC-Egypt 2022 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITC-Egypt55520.2022.9855681

@inproceedings{e07a17f9f27a4ac99af9349a73a2845c,

title = "Multi-Modal Wind Speed Modeling Using Mixture Probability Density Functions",

abstract = "For stochastic planning and operation of modern wind power farms, probability distribution functions (PDFs) are developed to estimate wind speed probabilities in a more predictable fashion. The parameters of the proposed model are obtained using the expectation-maximization (EM) algorithm to determine the model maximum log log-likelihood estimation (MLE). Additionally, the Bayesian information criterion (BIC), and Bootstrap Likelihood ratio test techniques are employed to analyze the appropriate number of PDFs and to determine the goodness of fit, respectively. The results demonstrate the accuracy of the proposed multi-modal PDFs compared to that of the well-known single PDFs utilized in the literature.",

keywords = "Bayesian information criterion (BIC), Expectation-maximization algorithm (EM), Maximum log-likelihood estimation (MLE), Multi-modal wind speed modeling, mixture PDFs, probability distribution function (PDF)",

author = "Mahzan Dalawir and Mohammad Borooshan and Ahmed Azab and Maher Azzouz and Awad, \{Ahmed S.A.\}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.",

year = "2022",

doi = "10.1109/ITC-Egypt55520.2022.9855681",

language = "English",

series = "International Telecommunications Conference, ITC-Egypt 2022 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "International Telecommunications Conference, ITC-Egypt 2022 - Proceedings",

address = "United States",

}

Dalawir, M, Borooshan, M, Azab, A, Azzouz, M & Awad, ASA 2022, Multi-Modal Wind Speed Modeling Using Mixture Probability Density Functions. in International Telecommunications Conference, ITC-Egypt 2022 - Proceedings. International Telecommunications Conference, ITC-Egypt 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ITC-Egypt55520.2022.9855681

Multi-Modal Wind Speed Modeling Using Mixture Probability Density Functions. / Dalawir, Mahzan; Borooshan, Mohammad; Azab, Ahmed et al.
International Telecommunications Conference, ITC-Egypt 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (International Telecommunications Conference, ITC-Egypt 2022 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Multi-Modal Wind Speed Modeling Using Mixture Probability Density Functions

AU - Dalawir, Mahzan

AU - Borooshan, Mohammad

AU - Azab, Ahmed

AU - Azzouz, Maher

AU - Awad, Ahmed S.A.

PY - 2022

Y1 - 2022

N2 - For stochastic planning and operation of modern wind power farms, probability distribution functions (PDFs) are developed to estimate wind speed probabilities in a more predictable fashion. The parameters of the proposed model are obtained using the expectation-maximization (EM) algorithm to determine the model maximum log log-likelihood estimation (MLE). Additionally, the Bayesian information criterion (BIC), and Bootstrap Likelihood ratio test techniques are employed to analyze the appropriate number of PDFs and to determine the goodness of fit, respectively. The results demonstrate the accuracy of the proposed multi-modal PDFs compared to that of the well-known single PDFs utilized in the literature.

AB - For stochastic planning and operation of modern wind power farms, probability distribution functions (PDFs) are developed to estimate wind speed probabilities in a more predictable fashion. The parameters of the proposed model are obtained using the expectation-maximization (EM) algorithm to determine the model maximum log log-likelihood estimation (MLE). Additionally, the Bayesian information criterion (BIC), and Bootstrap Likelihood ratio test techniques are employed to analyze the appropriate number of PDFs and to determine the goodness of fit, respectively. The results demonstrate the accuracy of the proposed multi-modal PDFs compared to that of the well-known single PDFs utilized in the literature.

KW - Bayesian information criterion (BIC)

KW - Expectation-maximization algorithm (EM)

KW - Maximum log-likelihood estimation (MLE)

KW - Multi-modal wind speed modeling

KW - mixture PDFs

KW - probability distribution function (PDF)

UR - https://www.scopus.com/pages/publications/85137754363

U2 - 10.1109/ITC-Egypt55520.2022.9855681

DO - 10.1109/ITC-Egypt55520.2022.9855681

M3 - Conference contribution

AN - SCOPUS:85137754363

T3 - International Telecommunications Conference, ITC-Egypt 2022 - Proceedings

BT - International Telecommunications Conference, ITC-Egypt 2022 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Dalawir M, Borooshan M, Azab A, Azzouz M, Awad ASA. Multi-Modal Wind Speed Modeling Using Mixture Probability Density Functions. In International Telecommunications Conference, ITC-Egypt 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. (International Telecommunications Conference, ITC-Egypt 2022 - Proceedings). doi: 10.1109/ITC-Egypt55520.2022.9855681

Multi-Modal Wind Speed Modeling Using Mixture Probability Density Functions

Abstract

Publication series

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this