Research Papers: Natural Gas Technology

Short-Term Forecasting of Natural Gas Consumption Using Factor Selection Algorithm and Optimized Support Vector Regression

[+] Author and Article Information
Nan Wei

College of Petroleum Engineering,
Southwest Petroleum University,
Chengdu 610500, Sichuan, China;
CNPC Key Laboratory of Oil & Gas
Storage and Transportation,
Southwest Petroleum University,
Chengdu 610500, Sichuan, China

Changjun Li

College of Petroleum Engineering,
Southwest Petroleum University,
Chengdu 610500, Sichuan, China;
CNPC Key Laboratory of Oil & Gas
Storage and Transportation,
Southwest Petroleum University,
Chengdu 610500, Sichuan, China
e-mail: lichangjunemail@sina.com

Chan Li

South Branch,
PetroChina Natural Gas Marketing Company,
Guangzhou 510000, Guangdong, China

Hanyu Xie

College of Petroleum Engineering,
Southwest Petroleum University,
Chengdu 610500, Sichuan, China;
CNPC Key Laboratory of Oil & Gas
Storage and Transportation,
Southwest Petroleum University,
Chengdu 610500, Sichuan, China

Zhongwei Du, Qiushi Zhang

Faculty of Engineering and Applied Science,
University of Regina,
Regina, SK S4S 0A2, Canada

Fanhua Zeng

Faculty of Engineering and Applied Science,
University of Regina,
Regina, SK S4S 0A2, Canada
e-mail: fanhua.zeng@uregina.ca

1Corresponding authors.

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received May 12, 2018; final manuscript received August 22, 2018; published online October 1, 2018. Editor: Hameed Metghalchi.

J. Energy Resour. Technol 141(3), 032701 (Oct 01, 2018) (10 pages) Paper No: JERT-18-1338; doi: 10.1115/1.4041413 History: Received May 12, 2018; Revised August 22, 2018

Forecasting of natural gas consumption has been essential for natural gas companies, customers, and governments. However, accurate forecasting of natural gas consumption is difficult, due to the cyclical change of the consumption and the complexity of the factors that influence the consumption. In this work, we constructed a hybrid artificial intelligence (AI) model to predict the short-term natural gas consumption and examine the effects of the factors in the consumption cycle. The proposed model combines factor selection algorithm (FSA), life genetic algorithm (LGA), and support vector regression (SVR), namely, as FSA-LGA-SVR. FSA is used to select factors automatically for different period based on correlation analysis. The LGA optimized SVR is utilized to provide the prediction of time series data. To avoid being trapped in local minima, the hyper-parameters of SVR are determined by LGA, which is enhanced due to newly added “learning” and “death” operations in conventional genetic algorithm. Additionally, in order to examine the effects of the factors in different period, we utilized the recent data of three big cities in Greece and divided the data into 12 subseries. The prediction results demonstrated that the proposed model can give a better performance of short-term natural gas consumption forecasting compared to the estimation value of existing models. Particularly, the mean absolute range normalized errors of the proposed model in Athens, Thessaloniki, and Larisa are 1.90%, 2.26%, and 2.12%, respectively.

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Grahic Jump Location
Fig. 1

Life genetic algorithm's individual

Grahic Jump Location
Fig. 3

The flow sheet LGA (initial life = 3)

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Fig. 4

The flow chart of the operation of FSA-LGA-SVR

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Fig. 5

The geography and natural gas consumption of Athens, Thessaloniki, and Larissa

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Fig. 6

The contribution of the factors from Jan. to Dec. in Athens (a), Thessaloniki (b), and Larisa (c).

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Fig. 7

Real and forecasting results of Athens (a), Thessaloniki (b), and Larisa (c)

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Fig. 8

The absolute error of the forecasting models in Athens (a), Thessaloniki (b), and Larisa (c)



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