基于最小二乘支持向量机的天然气负荷预测
NATURAL GAS LOAD FORECASTING BASED ON LEAST SQUARES SUPPORT VECTOR MACHINE
查看参考文献13篇
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文摘
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对城市天然气负荷预测的研究,对于保证天然气管网用气量、优化管网的调度和设备维修具有极其重要的意义.在国内,对于城市天然气负荷预测的研究才刚刚起步,目前还没有较系统的理论.同技术与理论较为成熟的电力负荷预测研究相比较,两者既有许多相同点,又有不同之处. |
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其他语种文摘
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Machine learning techniques are finding more and more applications in the field of load forecasting. A novel regression technique, called support vector machine (SVM), based on the statistical learning theory is applied in this paper for the prediction of natural gas demands. Least squares support vector machine (LS-SVM) is a kind of SVM that has different cost function with respect to SVM. SVM is based on the principle of structure risk minimization as opposed to the principle of empirical risk minimization supported by conventional regression techniques. The prediction result shows that the prediction accuracy of SVM is better than that of neural network. Thus, SVM appears to be a very promising prediction tool. The software package NGPSLF based on SVM prediction has been put into practical business application. |
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来源
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化工学报
,2004,55(5):828-832 【核心库】
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关键词
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结构风险最小化
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支持向量机
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最小二乘支持向量机
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支持向量回归
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负荷预测
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地址
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西安理工大学自动化与信息工程学院, 陕西, 西安, 710048
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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0438-1157 |
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学科
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自动化技术、计算机技术 |
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文献收藏号
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CSCD:1802515
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参考文献 共
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