Long-term wind power prediction based on rough set

Shuang Gao; Lei Dong; Xiao Zhong Liao; Yang Gao

doi:10.4028/www.scientific.net/AMM.329.411

Long-term wind power prediction based on rough set

Shuang Gao, Lei Dong, Xiao Zhong Liao, Yang Gao

School of Automation

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

2 Citations (Scopus)

Abstract

In long-term wind power prediction, dealing with the relevant factors correctly is the key point to improve the prediction accuracy. This paper presents a prediction method with rough set analysis. The key factors that affect the wind power prediction are identified by rough set theory. The chaotic characteristics of wind speed time series are analyzed. The rough set neural network prediction model is built by adding the key factors as the additional inputs to the chaotic neural network model. Data of Fujin wind farm are used for this paper to verify the new method of long-term wind power prediction. The results show that rough set method is a useful tool in long-term prediction of wind power.

Original language	English
Title of host publication	Advanced Technologies on Measure and Diagnosis, Manufacturing Systems and Environment Engineering
Pages	411-415
Number of pages	5
DOIs	https://doi.org/10.4028/www.scientific.net/AMM.329.411
Publication status	Published - 2013
Event	3rd International Conference on Intelligent Structure and Vibration Control, ISVC 2013 - Chongqing, China Duration: 22 Mar 2013 → 24 Mar 2013

Publication series

Name	Applied Mechanics and Materials
Volume	329
ISSN (Print)	1660-9336
ISSN (Electronic)	1662-7482

Conference

Conference	3rd International Conference on Intelligent Structure and Vibration Control, ISVC 2013
Country/Territory	China
City	Chongqing
Period	22/03/13 → 24/03/13

Keywords

Long-term prediction
Neural network
Rough set
Wind power prediction

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10.4028/www.scientific.net/AMM.329.411

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Gao, S., Dong, L., Liao, X. Z., & Gao, Y. (2013). Long-term wind power prediction based on rough set. In Advanced Technologies on Measure and Diagnosis, Manufacturing Systems and Environment Engineering (pp. 411-415). (Applied Mechanics and Materials; Vol. 329). https://doi.org/10.4028/www.scientific.net/AMM.329.411

@inproceedings{86c4e020e6d14d8ab7dc53142410fd81,

title = "Long-term wind power prediction based on rough set",

abstract = "In long-term wind power prediction, dealing with the relevant factors correctly is the key point to improve the prediction accuracy. This paper presents a prediction method with rough set analysis. The key factors that affect the wind power prediction are identified by rough set theory. The chaotic characteristics of wind speed time series are analyzed. The rough set neural network prediction model is built by adding the key factors as the additional inputs to the chaotic neural network model. Data of Fujin wind farm are used for this paper to verify the new method of long-term wind power prediction. The results show that rough set method is a useful tool in long-term prediction of wind power.",

keywords = "Long-term prediction, Neural network, Rough set, Wind power prediction",

author = "Shuang Gao and Lei Dong and Liao, {Xiao Zhong} and Yang Gao",

year = "2013",

doi = "10.4028/www.scientific.net/AMM.329.411",

language = "English",

isbn = "9783037857236",

series = "Applied Mechanics and Materials",

pages = "411--415",

booktitle = "Advanced Technologies on Measure and Diagnosis, Manufacturing Systems and Environment Engineering",

note = "3rd International Conference on Intelligent Structure and Vibration Control, ISVC 2013 ; Conference date: 22-03-2013 Through 24-03-2013",

}

Gao, S, Dong, L, Liao, XZ & Gao, Y 2013, Long-term wind power prediction based on rough set. in Advanced Technologies on Measure and Diagnosis, Manufacturing Systems and Environment Engineering. Applied Mechanics and Materials, vol. 329, pp. 411-415, 3rd International Conference on Intelligent Structure and Vibration Control, ISVC 2013, Chongqing, China, 22/03/13. https://doi.org/10.4028/www.scientific.net/AMM.329.411

TY - GEN

T1 - Long-term wind power prediction based on rough set

AU - Gao, Shuang

AU - Dong, Lei

AU - Liao, Xiao Zhong

AU - Gao, Yang

PY - 2013

Y1 - 2013

N2 - In long-term wind power prediction, dealing with the relevant factors correctly is the key point to improve the prediction accuracy. This paper presents a prediction method with rough set analysis. The key factors that affect the wind power prediction are identified by rough set theory. The chaotic characteristics of wind speed time series are analyzed. The rough set neural network prediction model is built by adding the key factors as the additional inputs to the chaotic neural network model. Data of Fujin wind farm are used for this paper to verify the new method of long-term wind power prediction. The results show that rough set method is a useful tool in long-term prediction of wind power.

AB - In long-term wind power prediction, dealing with the relevant factors correctly is the key point to improve the prediction accuracy. This paper presents a prediction method with rough set analysis. The key factors that affect the wind power prediction are identified by rough set theory. The chaotic characteristics of wind speed time series are analyzed. The rough set neural network prediction model is built by adding the key factors as the additional inputs to the chaotic neural network model. Data of Fujin wind farm are used for this paper to verify the new method of long-term wind power prediction. The results show that rough set method is a useful tool in long-term prediction of wind power.

KW - Long-term prediction

KW - Neural network

KW - Rough set

KW - Wind power prediction

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U2 - 10.4028/www.scientific.net/AMM.329.411

DO - 10.4028/www.scientific.net/AMM.329.411

M3 - Conference contribution

AN - SCOPUS:84880157760

SN - 9783037857236

T3 - Applied Mechanics and Materials

SP - 411

EP - 415

BT - Advanced Technologies on Measure and Diagnosis, Manufacturing Systems and Environment Engineering

T2 - 3rd International Conference on Intelligent Structure and Vibration Control, ISVC 2013

Y2 - 22 March 2013 through 24 March 2013

ER -

Long-term wind power prediction based on rough set

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Keywords

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