Markov Chain model for the stochastic behaviors of wind-direction data

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

Analyzing the behaviors of wind direction can complement knowledge concerning wind speed and help researchers draw conclusions regarding wind energy potential. Knowledge of the wind's direction enables the wind turbine to be positioned in such a way as to maximize the total amount of captured energy and optimize the wind farm's performance. In this paper, first-order and higher-order Markov chain models are proposed to describe the probabilistic behaviors of wind-direction data. A case study is conducted using data from Mersing, Malaysia. The wind-direction data are classified according to an eight-state Markov chain based on natural geographical directions. The model's parameters are estimated using the maximum likelihood method and the linear programming formulation. Several theoretical arguments regarding the model are also discussed. Finally, limiting probabilities are used to determine a long-run proportion of the wind directions generated. The results explain the dominant direction for Mersing's wind in terms of probability metrics.

Original languageEnglish
Pages (from-to)266-274
Number of pages9
JournalEnergy Conversion and Management
Volume92
DOIs
Publication statusPublished - 1 Mar 2015

Fingerprint

Markov processes
Wind turbines
Linear programming
Farms
Wind power
Maximum likelihood

Keywords

  • Markov chain
  • Probability model
  • Stochastic and probabilistic behaviors
  • Wind direction
  • Wind power

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Fuel Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment

Cite this

Markov Chain model for the stochastic behaviors of wind-direction data. / Masseran, Nurulkamal.

In: Energy Conversion and Management, Vol. 92, 01.03.2015, p. 266-274.

Research output: Contribution to journalArticle

@article{14389a863f0944839b9f2996dac7d9a3,
title = "Markov Chain model for the stochastic behaviors of wind-direction data",
abstract = "Analyzing the behaviors of wind direction can complement knowledge concerning wind speed and help researchers draw conclusions regarding wind energy potential. Knowledge of the wind's direction enables the wind turbine to be positioned in such a way as to maximize the total amount of captured energy and optimize the wind farm's performance. In this paper, first-order and higher-order Markov chain models are proposed to describe the probabilistic behaviors of wind-direction data. A case study is conducted using data from Mersing, Malaysia. The wind-direction data are classified according to an eight-state Markov chain based on natural geographical directions. The model's parameters are estimated using the maximum likelihood method and the linear programming formulation. Several theoretical arguments regarding the model are also discussed. Finally, limiting probabilities are used to determine a long-run proportion of the wind directions generated. The results explain the dominant direction for Mersing's wind in terms of probability metrics.",
keywords = "Markov chain, Probability model, Stochastic and probabilistic behaviors, Wind direction, Wind power",
author = "Nurulkamal Masseran",
year = "2015",
month = "3",
day = "1",
doi = "10.1016/j.enconman.2014.12.045",
language = "English",
volume = "92",
pages = "266--274",
journal = "Energy Conversion and Management",
issn = "0196-8904",
publisher = "Elsevier Limited",

}

TY - JOUR

T1 - Markov Chain model for the stochastic behaviors of wind-direction data

AU - Masseran, Nurulkamal

PY - 2015/3/1

Y1 - 2015/3/1

N2 - Analyzing the behaviors of wind direction can complement knowledge concerning wind speed and help researchers draw conclusions regarding wind energy potential. Knowledge of the wind's direction enables the wind turbine to be positioned in such a way as to maximize the total amount of captured energy and optimize the wind farm's performance. In this paper, first-order and higher-order Markov chain models are proposed to describe the probabilistic behaviors of wind-direction data. A case study is conducted using data from Mersing, Malaysia. The wind-direction data are classified according to an eight-state Markov chain based on natural geographical directions. The model's parameters are estimated using the maximum likelihood method and the linear programming formulation. Several theoretical arguments regarding the model are also discussed. Finally, limiting probabilities are used to determine a long-run proportion of the wind directions generated. The results explain the dominant direction for Mersing's wind in terms of probability metrics.

AB - Analyzing the behaviors of wind direction can complement knowledge concerning wind speed and help researchers draw conclusions regarding wind energy potential. Knowledge of the wind's direction enables the wind turbine to be positioned in such a way as to maximize the total amount of captured energy and optimize the wind farm's performance. In this paper, first-order and higher-order Markov chain models are proposed to describe the probabilistic behaviors of wind-direction data. A case study is conducted using data from Mersing, Malaysia. The wind-direction data are classified according to an eight-state Markov chain based on natural geographical directions. The model's parameters are estimated using the maximum likelihood method and the linear programming formulation. Several theoretical arguments regarding the model are also discussed. Finally, limiting probabilities are used to determine a long-run proportion of the wind directions generated. The results explain the dominant direction for Mersing's wind in terms of probability metrics.

KW - Markov chain

KW - Probability model

KW - Stochastic and probabilistic behaviors

KW - Wind direction

KW - Wind power

UR - http://www.scopus.com/inward/record.url?scp=84920652265&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84920652265&partnerID=8YFLogxK

U2 - 10.1016/j.enconman.2014.12.045

DO - 10.1016/j.enconman.2014.12.045

M3 - Article

AN - SCOPUS:84920652265

VL - 92

SP - 266

EP - 274

JO - Energy Conversion and Management

JF - Energy Conversion and Management

SN - 0196-8904

ER -