典型文献
A new method to forecast multi-time scale load of natural gas based on augmentation data-machine learning model
文献摘要：
Gas load forecasting is important for the economic and reliable operation of the city gas transmission and distribution system.In this paper,a nonlinear autoregressive model(NARX)with exogenous inputs,sup-port vector machine(SVM),Gaussian process regression(GPR)and ensemble tree model(ETREE)were used to predict and compare the gas load based on the gas load data in a certain region for past 3 years.The results showed that the prediction errors for most of days were higher than 10％.Further,simulation data were generated by considering the gas load variation trend,which was then combined with histor-ical data to form the augmentation data set to train the model.The test results indicated that the predic-tion error of daily gas load in one year reduced to below 7％with a machine learning prediction method based on augmentation data.In addition,the model based on augmentation data set still performed bet-ter than original data in predicting the monthly gas load in last year as well as daily gas load in last month and week.Therefore,the method based on augmentation data proposed in this paper is a potentially good tool to forecast natural gas load.
文献关键词：
中图分类号：
作者姓名：
Denglong Ma;Ruitao Wu;Zekang Li;Kang Cen;Jianmin Gao;Zaoxiao Zhang
作者机构：
School of Mechanical Engineering,Xi'an Jiaotong University,Xi'an 710049,China;School of Civil Engineering and Architecture,Southwest Petroleum University,Chengdu 610500,China;State Key Laboratory for Manufacturing System Engineering,Xi'an Jiaotong University,Xi'an 710049,China;School of Chemical Engineering and Technology,Xi'an Jiaotong University,Xi'an 710049,China
文献出处：
引用格式：
[1]Denglong Ma;Ruitao Wu;Zekang Li;Kang Cen;Jianmin Gao;Zaoxiao Zhang-.A new method to forecast multi-time scale load of natural gas based on augmentation data-machine learning model)[J].中国化学工程学报（英文版）,2022(08):166-175
A类：
ETREE
B类：
new,method,multi,scale,load,natural,gas,augmentation,data,machine,learning,model,Gas,forecasting,important,economic,reliable,operation,city,transmission,distribution,system,In,this,paper,nonlinear,autoregressive,NARX,exogenous,inputs,sup,vector,Gaussian,process,regression,GPR,ensemble,tree,were,used,compare,certain,region,past,years,results,showed,that,prediction,errors,most,days,higher,than,Further,simulation,generated,by,considering,variation,trend,which,was,then,combined,histor,ical,set,train,test,indicated,daily,one,reduced,below,addition,still,performed,bet,ter,original,predicting,monthly,last,well,week,Therefore,proposed,potentially,good,tool
AB值：
0.495298
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