| 基于支持向量机回归的天然气负荷预测研究 |
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| 引用本文： | 刘涵,刘丁,郑岗,梁炎明.基于支持向量机回归的天然气负荷预测研究[J].中国化学工程学报,2004,12(5):732-736. |
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| 作者姓名： | 刘涵 刘丁 郑岗 梁炎明 |
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| 作者单位： | SchoolofAutomationandInformationEngineering,Xi＇anUniversityofTechnology,Xi＇an710048,China |
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| 摘 要： | Natural gas load forecasting is a key process to the efficient operation of pipeline network. An accurate forecast is required to guarantee a balanced network operation and ensure safe gas supply at a minimum cost. Machine learning techniques have been increasingly applied to load forecasting. A novel regression technique based on the statistical learning theory, support vector machines (SVM), is investigated in this paper for natural gas shortterm load forecasting. SVM is based on the principle of structure risk minimization as opposed to the principle of empirical risk minimization in conventional regression techniques. Using a data set with 2 years load values we developed prediction model using SVM to obtain 31 days load predictions. The results on city natural gas short-term load forecasting show that SVM provides better prediction accuracy than neural network. The software package natural gas pipeline networks simulation and load forecasting (NGPNSLF) based on support vector regression prediction has been developed, which has also been applied in practice.
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| 关 键 词： | SVM 支持向量机 回归 负荷预测 |
| 修稿时间： | |
Research on Natural Gas Short-Term Load Forecasting Based on Support Vector Regression |
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| LIU Han,LIU Ding,ZHENG Gang,LIANG Yanming.Research on Natural Gas Short-Term Load Forecasting Based on Support Vector Regression[J].Chinese Journal of Chemical Engineering,2004,12(5):732-736. |
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| Authors: | LIU Han LIU Ding ZHENG Gang LIANG Yanming |
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| Affiliation: | School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China;School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China;School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China;School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China |
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| Abstract: | Natural gas load forecasting is a key process to the efficient operation of pipeline network. An accurate forecast is required to guarantee a balanced network operation and ensure safe gas supply at a minimum cost.Machine learning techniques have been increasingly applied to load forecasting. A novel regression technique based on the statistical learning theory, support vector machines (SVM), is investigated in this paper for natural gas shortterm load forecasting. SVM is based on the principle of structure risk minimization as opposed to the principle of empirical risk minimization in conventional regression techniques. Using a data set with 2 years load values we developed prediction model using SVM to obtain 31 days load predictions. The results on city natural gas short-term load forecasting show that SVM provides better prediction accuracy than neural network. The software package natural gas pipeline networks simulation and load forecasting (NGPNSLF) based on support vector regression prediction has been developed, which has also been applied in practice. |
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| Keywords: | structure risk minimization support vector machines support vector regression load forecasting neural network |
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