Application of GMDH to short-term load forecasting

Hongya Xu; Yao Dong; Jie Wu; Weigang Zhao

doi:10.1007/978-3-642-27869-3_4

Application of GMDH to short-term load forecasting

Hongya Xu, Yao Dong, Jie Wu^*, Weigang Zhao

^*Corresponding author for this work

Lanzhou University

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

10 Citations (Scopus)

Abstract

Daily power load forecasting plays a significant role in electrical power system operation and planning. Therefore, it is necessary to find automatic interrelations of data and select the optimal structure of model. However, obtaining high accuracy by using single model for short-term load forecasting (STLF) is not easy. In this paper, Group Method of Data Handling (GMDH) is applied to forecast electric load demand of New South Wales (NSW) in Australia from January 17, 2009 to January 18, 2009. Compared with outcomes obtained by ARIMA, we demonstrate that GMDH is a better method for STLF.

Original language	English
Title of host publication	Advances in Intelligent Systems - Selected Papers from 2012 International Conference on Control Systems, ICCS 2012
Pages	27-32
Number of pages	6
DOIs	https://doi.org/10.1007/978-3-642-27869-3_4
Publication status	Published - 2012
Externally published	Yes
Event	2012 International Conference on Environment Science, ICES 2012 and 2012 International Conference on Computer Science, ICCS 2012 - Melbourne, VIC, Australia Duration: 15 Mar 2012 → 16 Mar 2012

Publication series

Name	Advances in Intelligent and Soft Computing
Volume	138 AISC
ISSN (Print)	1867-5662

Conference

Conference	2012 International Conference on Environment Science, ICES 2012 and 2012 International Conference on Computer Science, ICCS 2012
Country/Territory	Australia
City	Melbourne, VIC
Period	15/03/12 → 16/03/12

Keywords

ARIMA
Group Method of Data Handling (GMDH)
short-term load forecasting (STLF)

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10.1007/978-3-642-27869-3_4

Cite this

Xu, H., Dong, Y., Wu, J., & Zhao, W. (2012). Application of GMDH to short-term load forecasting. In Advances in Intelligent Systems - Selected Papers from 2012 International Conference on Control Systems, ICCS 2012 (pp. 27-32). (Advances in Intelligent and Soft Computing; Vol. 138 AISC). https://doi.org/10.1007/978-3-642-27869-3_4

@inproceedings{3be3856b4ec64b558d1cdcd10945ae4b,

title = "Application of GMDH to short-term load forecasting",

abstract = "Daily power load forecasting plays a significant role in electrical power system operation and planning. Therefore, it is necessary to find automatic interrelations of data and select the optimal structure of model. However, obtaining high accuracy by using single model for short-term load forecasting (STLF) is not easy. In this paper, Group Method of Data Handling (GMDH) is applied to forecast electric load demand of New South Wales (NSW) in Australia from January 17, 2009 to January 18, 2009. Compared with outcomes obtained by ARIMA, we demonstrate that GMDH is a better method for STLF.",

keywords = "ARIMA, Group Method of Data Handling (GMDH), short-term load forecasting (STLF)",

author = "Hongya Xu and Yao Dong and Jie Wu and Weigang Zhao",

year = "2012",

doi = "10.1007/978-3-642-27869-3_4",

language = "English",

isbn = "9783642278686",

series = "Advances in Intelligent and Soft Computing",

pages = "27--32",

booktitle = "Advances in Intelligent Systems - Selected Papers from 2012 International Conference on Control Systems, ICCS 2012",

note = "2012 International Conference on Environment Science, ICES 2012 and 2012 International Conference on Computer Science, ICCS 2012 ; Conference date: 15-03-2012 Through 16-03-2012",

}

Xu, H, Dong, Y, Wu, J & Zhao, W 2012, Application of GMDH to short-term load forecasting. in Advances in Intelligent Systems - Selected Papers from 2012 International Conference on Control Systems, ICCS 2012. Advances in Intelligent and Soft Computing, vol. 138 AISC, pp. 27-32, 2012 International Conference on Environment Science, ICES 2012 and 2012 International Conference on Computer Science, ICCS 2012, Melbourne, VIC, Australia, 15/03/12. https://doi.org/10.1007/978-3-642-27869-3_4

Application of GMDH to short-term load forecasting. / Xu, Hongya; Dong, Yao; Wu, Jie et al.
Advances in Intelligent Systems - Selected Papers from 2012 International Conference on Control Systems, ICCS 2012. 2012. p. 27-32 (Advances in Intelligent and Soft Computing; Vol. 138 AISC).

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

TY - GEN

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AU - Dong, Yao

AU - Wu, Jie

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N2 - Daily power load forecasting plays a significant role in electrical power system operation and planning. Therefore, it is necessary to find automatic interrelations of data and select the optimal structure of model. However, obtaining high accuracy by using single model for short-term load forecasting (STLF) is not easy. In this paper, Group Method of Data Handling (GMDH) is applied to forecast electric load demand of New South Wales (NSW) in Australia from January 17, 2009 to January 18, 2009. Compared with outcomes obtained by ARIMA, we demonstrate that GMDH is a better method for STLF.

AB - Daily power load forecasting plays a significant role in electrical power system operation and planning. Therefore, it is necessary to find automatic interrelations of data and select the optimal structure of model. However, obtaining high accuracy by using single model for short-term load forecasting (STLF) is not easy. In this paper, Group Method of Data Handling (GMDH) is applied to forecast electric load demand of New South Wales (NSW) in Australia from January 17, 2009 to January 18, 2009. Compared with outcomes obtained by ARIMA, we demonstrate that GMDH is a better method for STLF.

KW - ARIMA

KW - Group Method of Data Handling (GMDH)

KW - short-term load forecasting (STLF)

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SN - 9783642278686

T3 - Advances in Intelligent and Soft Computing

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BT - Advances in Intelligent Systems - Selected Papers from 2012 International Conference on Control Systems, ICCS 2012

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Y2 - 15 March 2012 through 16 March 2012

ER -

Application of GMDH to short-term load forecasting

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Keywords

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