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A new method to forecast multi-time scale load of natural gas based on augmentation data-machine learning model

Chinese Journal of Chemical Engineering 2022年第8期35卷 166-175页

作者： Denglong Ma Ruitao Wu Zekang Li Kang Cen Jianmin Gao Zaoxiao Zhang School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an 710049China School of Civil Engineering and Architecture Southwest Petroleum UniversityChengdu 610500China School of Chemical Engineering and Technology Xi’an Jiaotong UniversityXi’an 710049China State Key Laboratory for Manufacturing System Engineering Xi’an Jiaotong UniversityXi’an 710049China

Gas load forecasting is important for the economic and reliable operation of the city gas transmission and distribution *** this paper,a nonlinear autoregressive model(NARX)with exogenous inputs,support 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 *** 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 historical data to form the augmentation data set to train the *** test results indicated that the prediction error of daily gas load in one year reduced to below 7%with a machine learning prediction method based on augmentation *** addition,the model based on augmentation data set still performed better than original data in predicting the monthly gas load in last year as well as daily gas load in last month and ***,the method based on augmentation data proposed in this paper is a potentially good tool to forecast natural gas load.

关键词： Natural gas Machine learning Prediction Neural network augmentation data

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