基于LS-SVM的天然气管网负荷组合预测  

Combination Method of Natural Gas Pipeline Network Load Forecasting Based on Least Squares Support Vector Machines

作　　者：代小华[1] 白建辉[2] 杨玲 唐小江[4] 

机构地区：[1]中国石油天然气管道工程有限公司工艺室,河北廊坊065000 [2]中国石油勘探开发研究院,北京100000 [3]中冶成工建设有限公司新疆分公司,新疆乌鲁木齐830000 [4]中国石油管道局物质装备公司,河北廊坊065000

出　　处：《管道技术与设备》2010年第3期14-16,33,共4页Pipeline Technique and Equipment

摘　　要：针对现有组合预测模型,基于经验风险最小化原则,克服预测精度受组合模型限制的缺点,提出一种基于最小二乘支持向量机(LS-SVM)的天然气管网负荷组合预测模型,并与AR模型、BP神经网络模型、GM(1,1)模型以及最优权重组合模型进行了比较,得出基于最小二乘支持向量机的天然气管网负荷组合预测模型能够得到更高的预测精确度,可为天然气管网的安全运行以及优化调度提供决策支持的结论。In light of the existing combined prediction model based on the experience of risk minimization and of the forecast accuracy of the model by the combination of restrictions,a natural gas pipe network load forecasting model based on the least squares support vector machines（LS-SVM）is proposed and compared with the AR model,BP neural network model,GM（1,1）model as well as the top priority recombination model.Least squares support vector machines based on the natural gas pipeline network load forecasting model portfolio will provide a higher forecast accuracy for the safe operation of the pipeline network optimization as well as credible support for the theory.

关 键 词：天然气 管网 负荷 组合预测 最小二乘支持向量机 

分 类 号：TE83[石油与天然气工程—油气储运工程]