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

Denglong Ma; Ruitao Wu; Zekang Li; Kang Cen; Jianmin Gao; Zaoxiao Zhang

doi:10.1016/j.cjche.2021.11.023

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

Updated：2026-01-06

- A new method to forecast multi-time scale load of natural gas based on augmentation data-machine learning model
- Chinese Journal of Chemical Engineering Vol. 48, Issue 8, Pages: 166-175(2022)
- Affiliations：
  
  1. School of Mechanical Engineering, Xi’an Jiaotong University,Xi’an,China,710049
  2. School of Civil Engineering and Architecture, Southwest Petroleum University,Chengdu,China,610500
  3. State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University,Xi’an,China,710049
  4. School of Chemical Engineering and Technology, Xi’an Jiaotong University,Xi’an,China,710049
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2021.11.023
  CLC：
- Published：2022
- Accepted：
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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]. Chinese Journal of Chemical Engineering, 2022, 48(8): 166-175.
DOI：

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]. Chinese Journal of Chemical Engineering, 2022, 48(8): 166-175. DOI： 10.1016/j.cjche.2021.11.023.

摘要

Abstract

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

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?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 historical data to form the augmentation data set to train the model. 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 data. In 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 week. Therefore

the method based on augmentation data proposed in this paper is a potentially good tool to forecast natural gas load.

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