Next day load forecasting using SVM

Xunming Li; Dengcai Gong; Linfeng Li; Changyin Sun

doi:10.1007/11427469_101

Next day load forecasting using SVM

Xunming Li
, Dengcai Gong
, Linfeng Li
, Changyin Sun

Hohai University

Research output: Contribution to journal › Conference article › peer-review

22 Scopus citations

Abstract

Based on similar day method and SVM, this paper proposes a new method for next day load forecasting. The new method uses the parameters of several similar days, instead of only selecting one similar day as in similar day method. The parameters of selected similar days are used as inputs to SVM for forecasting the loads of 24 points (one hour per point) of the next day. The method behaves the advantages of both similar day method and SVM method, Corresponding software was developed and used to forecast the next day load in a practical power system and the final forecasting error is low.

Original language	English
Pages (from-to)	634-639
Number of pages	6
Journal	Lecture Notes in Computer Science
Volume	3498
Issue number	III
DOIs	https://doi.org/10.1007/11427469_101
State	Published - 2005
Externally published	Yes
Event	Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005 - Chongqing, China Duration: 30 May 2005 → 1 Jun 2005

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10.1007/11427469_101

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title = "Next day load forecasting using SVM",

abstract = "Based on similar day method and SVM, this paper proposes a new method for next day load forecasting. The new method uses the parameters of several similar days, instead of only selecting one similar day as in similar day method. The parameters of selected similar days are used as inputs to SVM for forecasting the loads of 24 points (one hour per point) of the next day. The method behaves the advantages of both similar day method and SVM method, Corresponding software was developed and used to forecast the next day load in a practical power system and the final forecasting error is low.",

author = "Xunming Li and Dengcai Gong and Linfeng Li and Changyin Sun",

year = "2005",

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note = "Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005 ; Conference date: 30-05-2005 Through 01-06-2005",

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AU - Li, Xunming

AU - Gong, Dengcai

AU - Li, Linfeng

AU - Sun, Changyin

PY - 2005

Y1 - 2005

N2 - Based on similar day method and SVM, this paper proposes a new method for next day load forecasting. The new method uses the parameters of several similar days, instead of only selecting one similar day as in similar day method. The parameters of selected similar days are used as inputs to SVM for forecasting the loads of 24 points (one hour per point) of the next day. The method behaves the advantages of both similar day method and SVM method, Corresponding software was developed and used to forecast the next day load in a practical power system and the final forecasting error is low.

AB - Based on similar day method and SVM, this paper proposes a new method for next day load forecasting. The new method uses the parameters of several similar days, instead of only selecting one similar day as in similar day method. The parameters of selected similar days are used as inputs to SVM for forecasting the loads of 24 points (one hour per point) of the next day. The method behaves the advantages of both similar day method and SVM method, Corresponding software was developed and used to forecast the next day load in a practical power system and the final forecasting error is low.

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U2 - 10.1007/11427469_101

DO - 10.1007/11427469_101

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SN - 0302-9743

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IS - III

T2 - Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005

Y2 - 30 May 2005 through 1 June 2005

ER -

Next day load forecasting using SVM

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