An annual load forecasting model based on support vector regression with differential evolution algorithm

Annual load forecasting is very important for the electric power industry. As influenced by various factors, an annual load curve shows a non-linear characteristic, which demonstrates that the annual load forecasting is a non-linear problem. Support vector regression (SVR) is proven to be useful in...

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Veröffentlicht in:	Applied energy Jg. 94; S. 65 - 70
Hauptverfasser:	Wang, Jianjun, Li, Li, Niu, Dongxiao, Tan, Zhongfu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 01.06.2012 Elsevier
Schlagworte:	algorithms Annual load forecasting Applied sciences Differential evolution (DE) electric power Energy Exact sciences and technology industry neural networks Support vector regression (SVR) Annual load forecasting Support vector regression (SVR) Differential evolution (DE)
ISSN:	0306-2619, 1872-9118
Online-Zugang:	Volltext
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Abstract	Annual load forecasting is very important for the electric power industry. As influenced by various factors, an annual load curve shows a non-linear characteristic, which demonstrates that the annual load forecasting is a non-linear problem. Support vector regression (SVR) is proven to be useful in dealing with non-linear forecasting problems in recent years. The key point in using SVR for forecasting is how to determine the appropriate parameters. This paper proposes a hybrid load forecasting model combining differential evolution (DE) algorithm and support vector regression to deal with this problem, where the DE algorithm is used to choose the appropriate parameters for the SVR load forecasting model. The effectiveness of this model has been proved by the final simulation which shows that the proposed model outperforms the SVR model with default parameters, back propagation artificial neural network (BPNN) and regression forecasting models in the annual load forecasting.
AbstractList	Annual load forecasting is very important for the electric power industry. As influenced by various factors, an annual load curve shows a non-linear characteristic, which demonstrates that the annual load forecasting is a non-linear problem. Support vector regression (SVR) is proven to be useful in dealing with non-linear forecasting problems in recent years. The key point in using SVR for forecasting is how to determine the appropriate parameters. This paper proposes a hybrid load forecasting model combining differential evolution (DE) algorithm and support vector regression to deal with this problem, where the DE algorithm is used to choose the appropriate parameters for the SVR load forecasting model. The effectiveness of this model has been proved by the final simulation which shows that the proposed model outperforms the SVR model with default parameters, back propagation artificial neural network (BPNN) and regression forecasting models in the annual load forecasting.
Author	Niu, Dongxiao Li, Li Wang, Jianjun Tan, Zhongfu
Author_xml	– sequence: 1 givenname: Jianjun surname: Wang fullname: Wang, Jianjun email: wangjianjunhd@gmail.com organization: School of Economic and Management Administration, North China Electric Power University, Beijing 102206, China – sequence: 2 givenname: Li surname: Li fullname: Li, Li organization: School of Economics & Business Administration, Beijing Information Science & Technology University, Beijing 100085, China – sequence: 3 givenname: Dongxiao surname: Niu fullname: Niu, Dongxiao organization: School of Economic and Management Administration, North China Electric Power University, Beijing 102206, China – sequence: 4 givenname: Zhongfu surname: Tan fullname: Tan, Zhongfu organization: School of Economic and Management Administration, North China Electric Power University, Beijing 102206, China
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Snippet	Annual load forecasting is very important for the electric power industry. As influenced by various factors, an annual load curve shows a non-linear...
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SubjectTerms	algorithms Annual load forecasting Applied sciences Differential evolution (DE) electric power Energy Exact sciences and technology industry neural networks Support vector regression (SVR)
Title	An annual load forecasting model based on support vector regression with differential evolution algorithm
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