Power system load forecasting based upon combination of Markov Chain fuzzy clustering

Xun Chen; La Yuan Xu; Xue Mei Ren

Power system load forecasting based upon combination of Markov Chain fuzzy clustering

Xun Chen^*, La Yuan Xu, Xue Mei Ren

^*Corresponding author for this work

School of Automation

Research output: Contribution to conference › Paper › peer-review

Abstract

A new method based on the combination of fuzzy clustering and Markov Chain Models is presented in this paper, To different types of random phenomena of in time series, several functions are built respectively. State analysis of object is carried out by using Markov Chain, while fuzzy clustering is employed to the states of samples to suit the real case, then according to state transfer, the load change is predicted, The new algorithm which is used in load forecasting firstly reaches the global optimum, when the time series have strongly properties of random, the algorithm works well. The simulation results show that the error is below the level of 3.5% in most the case.

Original language	English
Pages	5138-5141
Number of pages	4
Publication status	Published - 2004
Event	WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings - Hangzhou, China Duration: 15 Jun 2004 → 19 Jun 2004

Conference

Conference	WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings
Country/Territory	China
City	Hangzhou
Period	15/06/04 → 19/06/04

Keywords

Combined forecasting
Fuzzy clustering
Markov Chain

Cite this

@conference{ab13e6e69a914189abbfaca1bdd7575a,

title = "Power system load forecasting based upon combination of Markov Chain fuzzy clustering",

abstract = "A new method based on the combination of fuzzy clustering and Markov Chain Models is presented in this paper, To different types of random phenomena of in time series, several functions are built respectively. State analysis of object is carried out by using Markov Chain, while fuzzy clustering is employed to the states of samples to suit the real case, then according to state transfer, the load change is predicted, The new algorithm which is used in load forecasting firstly reaches the global optimum, when the time series have strongly properties of random, the algorithm works well. The simulation results show that the error is below the level of 3.5% in most the case.",

keywords = "Combined forecasting, Fuzzy clustering, Markov Chain",

author = "Xun Chen and Xu, {La Yuan} and Ren, {Xue Mei}",

year = "2004",

language = "English",

pages = "5138--5141",

note = "WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings ; Conference date: 15-06-2004 Through 19-06-2004",

}

TY - CONF

T1 - Power system load forecasting based upon combination of Markov Chain fuzzy clustering

AU - Chen, Xun

AU - Xu, La Yuan

AU - Ren, Xue Mei

PY - 2004

Y1 - 2004

N2 - A new method based on the combination of fuzzy clustering and Markov Chain Models is presented in this paper, To different types of random phenomena of in time series, several functions are built respectively. State analysis of object is carried out by using Markov Chain, while fuzzy clustering is employed to the states of samples to suit the real case, then according to state transfer, the load change is predicted, The new algorithm which is used in load forecasting firstly reaches the global optimum, when the time series have strongly properties of random, the algorithm works well. The simulation results show that the error is below the level of 3.5% in most the case.

AB - A new method based on the combination of fuzzy clustering and Markov Chain Models is presented in this paper, To different types of random phenomena of in time series, several functions are built respectively. State analysis of object is carried out by using Markov Chain, while fuzzy clustering is employed to the states of samples to suit the real case, then according to state transfer, the load change is predicted, The new algorithm which is used in load forecasting firstly reaches the global optimum, when the time series have strongly properties of random, the algorithm works well. The simulation results show that the error is below the level of 3.5% in most the case.

KW - Combined forecasting

KW - Fuzzy clustering

KW - Markov Chain

UR - http://www.scopus.com/inward/record.url?scp=4444379522&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:4444379522

SP - 5138

EP - 5141

T2 - WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings

Y2 - 15 June 2004 through 19 June 2004

ER -

Power system load forecasting based upon combination of Markov Chain fuzzy clustering

Abstract

Conference

Keywords

Other files and links

Fingerprint

Cite this