Modeling wind speed using probability distribution function, Markov and ARMA models

Asad Bizrah; Mohammad Almuhaini

doi:10.1109/PESGM.2015.7286273

Modeling wind speed using probability distribution function, Markov and ARMA models

Asad Bizrah
, Mohammad Almuhaini

Department of Electrical Engineering

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

7 Scopus citations

Abstract

Power utilities' demand for renewable energy resources is increasing, but the complexities of integrating conventional and renewable resources into the active networked distribution system can be prohibitive. Wind power is the most popular renewable resource worldwide due to its efficiency and cost. Effective planning of wind power integration requires accurate wind speed modeling. However, achieving this accuracy is challenging. New research is needed to determine the most accurate modeling method for wind power. In this paper, probability distribution function (pdf), Markov chain and Auto-Regressive Moving Average (ARMA) are evaluated for accuracy in modeling to forecast short- and long-term wind speed and power, and the ease of integrating wind power into the system.

Original language	English
Title of host publication	2015 IEEE Power and Energy Society General Meeting, PESGM 2015
Publisher	IEEE Computer Society
ISBN (Electronic)	9781467380409
DOIs	https://doi.org/10.1109/PESGM.2015.7286273
State	Published - 30 Sep 2015

Publication series

Name	IEEE Power and Energy Society General Meeting
Volume	2015-September
ISSN (Print)	1944-9925
ISSN (Electronic)	1944-9933

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

ARMA
Goodness-of-fit
Markov Chain
Wind Speed

ASJC Scopus subject areas

Energy Engineering and Power Technology
Nuclear Energy and Engineering
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering

UN SDGs

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

Access to Document

10.1109/PESGM.2015.7286273

Cite this

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title = "Modeling wind speed using probability distribution function, Markov and ARMA models",

abstract = "Power utilities' demand for renewable energy resources is increasing, but the complexities of integrating conventional and renewable resources into the active networked distribution system can be prohibitive. Wind power is the most popular renewable resource worldwide due to its efficiency and cost. Effective planning of wind power integration requires accurate wind speed modeling. However, achieving this accuracy is challenging. New research is needed to determine the most accurate modeling method for wind power. In this paper, probability distribution function (pdf), Markov chain and Auto-Regressive Moving Average (ARMA) are evaluated for accuracy in modeling to forecast short- and long-term wind speed and power, and the ease of integrating wind power into the system.",

keywords = "ARMA, Goodness-of-fit, Markov Chain, Wind Speed",

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doi = "10.1109/PESGM.2015.7286273",

language = "English",

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Modeling wind speed using probability distribution function, Markov and ARMA models. / Bizrah, Asad; Almuhaini, Mohammad.
2015 IEEE Power and Energy Society General Meeting, PESGM 2015. IEEE Computer Society, 2015. 7286273 (IEEE Power and Energy Society General Meeting; Vol. 2015-September).

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

TY - GEN

T1 - Modeling wind speed using probability distribution function, Markov and ARMA models

AU - Bizrah, Asad

AU - Almuhaini, Mohammad

PY - 2015/9/30

Y1 - 2015/9/30

N2 - Power utilities' demand for renewable energy resources is increasing, but the complexities of integrating conventional and renewable resources into the active networked distribution system can be prohibitive. Wind power is the most popular renewable resource worldwide due to its efficiency and cost. Effective planning of wind power integration requires accurate wind speed modeling. However, achieving this accuracy is challenging. New research is needed to determine the most accurate modeling method for wind power. In this paper, probability distribution function (pdf), Markov chain and Auto-Regressive Moving Average (ARMA) are evaluated for accuracy in modeling to forecast short- and long-term wind speed and power, and the ease of integrating wind power into the system.

AB - Power utilities' demand for renewable energy resources is increasing, but the complexities of integrating conventional and renewable resources into the active networked distribution system can be prohibitive. Wind power is the most popular renewable resource worldwide due to its efficiency and cost. Effective planning of wind power integration requires accurate wind speed modeling. However, achieving this accuracy is challenging. New research is needed to determine the most accurate modeling method for wind power. In this paper, probability distribution function (pdf), Markov chain and Auto-Regressive Moving Average (ARMA) are evaluated for accuracy in modeling to forecast short- and long-term wind speed and power, and the ease of integrating wind power into the system.

KW - ARMA

KW - Goodness-of-fit

KW - Markov Chain

KW - Wind Speed

UR - https://www.scopus.com/pages/publications/84956870104

U2 - 10.1109/PESGM.2015.7286273

DO - 10.1109/PESGM.2015.7286273

M3 - Conference contribution

AN - SCOPUS:84956870104

T3 - IEEE Power and Energy Society General Meeting

BT - 2015 IEEE Power and Energy Society General Meeting, PESGM 2015

PB - IEEE Computer Society

ER -

Modeling wind speed using probability distribution function, Markov and ARMA models

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this