Wind park power forecasting models and comparison

Qian Zhang; Kin Keung Lai; Dongxiao Niu; Qiang Zang

doi:10.1109/CSO.2012.15

Wind park power forecasting models and comparison

Qian Zhang, Kin Keung Lai, Dongxiao Niu, Qiang Zang

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

4 Citations (Scopus)

Abstract

In this paper, four new forecasting models - univariate LS-SVM model and three hybrid models of ARIMA and LS-SVM models are introduced for wind power output forecasting. Historical data of 78 wind farms are used to compare and evaluate the performance of the best models. Empirical analysis indicates that the proposed univariate LSSVM model and hybrid models can not significantly outperform linear models but they are not inferior to linear models. © 2012 IEEE.

Original language	English
Title of host publication	Proceedings of the 2012 5th International Joint Conference on Computational Sciences and Optimization, CSO 2012
Pages	27-31
DOIs	https://doi.org/10.1109/CSO.2012.15
Publication status	Published - 2012
Event	2012 5th International Joint Conference on Computational Sciences and Optimization, CSO 2012 - Harbin, Heilongjiang, China Duration: 23 Jun 2012 → 26 Jun 2012

Conference

Conference	2012 5th International Joint Conference on Computational Sciences and Optimization, CSO 2012
Place	China
City	Harbin, Heilongjiang
Period	23/06/12 → 26/06/12

Research Keywords

ARIMA
Hybrid models
LS-SVM
Wind power forecasting

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

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10.1109/CSO.2012.15

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@inproceedings{991dfff65aa34ed798ab6850c12761fd,

title = "Wind park power forecasting models and comparison",

abstract = "In this paper, four new forecasting models - univariate LS-SVM model and three hybrid models of ARIMA and LS-SVM models are introduced for wind power output forecasting. Historical data of 78 wind farms are used to compare and evaluate the performance of the best models. Empirical analysis indicates that the proposed univariate LSSVM model and hybrid models can not significantly outperform linear models but they are not inferior to linear models. {\textcopyright} 2012 IEEE.",

keywords = "ARIMA, Hybrid models, LS-SVM, Wind power forecasting",

author = "Qian Zhang and Lai, {Kin Keung} and Dongxiao Niu and Qiang Zang",

year = "2012",

doi = "10.1109/CSO.2012.15",

language = "English",

isbn = "9780769546902",

pages = "27--31",

booktitle = "Proceedings of the 2012 5th International Joint Conference on Computational Sciences and Optimization, CSO 2012",

note = "2012 5th International Joint Conference on Computational Sciences and Optimization, CSO 2012 ; Conference date: 23-06-2012 Through 26-06-2012",

}

Zhang, Q, Lai, KK, Niu, D & Zang, Q 2012, Wind park power forecasting models and comparison. in Proceedings of the 2012 5th International Joint Conference on Computational Sciences and Optimization, CSO 2012., 6274671, pp. 27-31, 2012 5th International Joint Conference on Computational Sciences and Optimization, CSO 2012, Harbin, Heilongjiang, China, 23/06/12. https://doi.org/10.1109/CSO.2012.15

Wind park power forecasting models and comparison. / Zhang, Qian; Lai, Kin Keung; Niu, Dongxiao et al.
Proceedings of the 2012 5th International Joint Conference on Computational Sciences and Optimization, CSO 2012. 2012. p. 27-31 6274671.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Wind park power forecasting models and comparison

AU - Zhang, Qian

AU - Lai, Kin Keung

AU - Niu, Dongxiao

AU - Zang, Qiang

PY - 2012

Y1 - 2012

N2 - In this paper, four new forecasting models - univariate LS-SVM model and three hybrid models of ARIMA and LS-SVM models are introduced for wind power output forecasting. Historical data of 78 wind farms are used to compare and evaluate the performance of the best models. Empirical analysis indicates that the proposed univariate LSSVM model and hybrid models can not significantly outperform linear models but they are not inferior to linear models. © 2012 IEEE.

AB - In this paper, four new forecasting models - univariate LS-SVM model and three hybrid models of ARIMA and LS-SVM models are introduced for wind power output forecasting. Historical data of 78 wind farms are used to compare and evaluate the performance of the best models. Empirical analysis indicates that the proposed univariate LSSVM model and hybrid models can not significantly outperform linear models but they are not inferior to linear models. © 2012 IEEE.

KW - ARIMA

KW - Hybrid models

KW - LS-SVM

KW - Wind power forecasting

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84867919530&origin=recordpage

U2 - 10.1109/CSO.2012.15

DO - 10.1109/CSO.2012.15

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9780769546902

SP - 27

EP - 31

BT - Proceedings of the 2012 5th International Joint Conference on Computational Sciences and Optimization, CSO 2012

T2 - 2012 5th International Joint Conference on Computational Sciences and Optimization, CSO 2012

Y2 - 23 June 2012 through 26 June 2012

ER -

Wind park power forecasting models and comparison

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Research Keywords

UN SDGs

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