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research-article

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, Sichuan, 610500, China; CNPC Key Laboratory of Oil & Gas Storage and Transportation, Southwest Petroleum University, Chengdu, Sichuan, 610500, China
joey_weinan@126.com

Changjun Li

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

CHAN LI

South Branch, PetroChina Natural Gas Marketing Company, Guangzhou, Guangdong, 510000, China
499317146@qq.com

Hanyu Xie

College of Petroleum Engineering, Southwest Petroleum University, Chengdu, Sichuan, 610500, China; CNPC Key Laboratory of Oil & Gas Storage and Transportation, Southwest Petroleum University, Chengdu, Sichuan, 610500, China
402193910@qq.com

Zhongwei Du

Faculty of Engineering and Applied Science, University of Regina, SK, S4S 0A2, Canada
du225@uregina.ca

Qiushi Zhang

Faculty of Engineering and Applied Science, University of Regina, SK, S4S 0A2, Canada
qzc089@uregina.ca

Fanhua Zeng

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

1Corresponding author.

ASME doi:10.1115/1.4041413 History: Received May 12, 2018; Revised August 22, 2018

Abstract

Forecasting of natural gas consumption has been essential for natural gas companies, customers and governments. However, accurately forecast 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 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.

Copyright (c) 2018 by ASME
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