Study on Natural Gas Demand Prediction Model in China-Reference-Cited by-同舟云学术

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Study on Natural Gas Demand Prediction Model in China

Published:2013-12 Issue: Volume:869-870 Page:533-536
ISSN:1662-8985
Container-title:Advanced Materials Research
language:
Short-container-title:AMR

Author:

Feng Xue¹,Zhang Jin Suo¹,Zou Shao Hui¹,Bao Wuyunbilige¹

Affiliation:

1. Xian University of Science and Technology

Abstract

Based on the characteristics of natural gas demand trend, this paper proposed ARIMA model which can predict China's natural gas demand as an effective tool. Compared with the RBF neural network model and combined model, empirical results show that the accuracy and stability of the ARIMA model is best.

Publisher

Trans Tech Publications, Ltd.

Subject

General Engineering

Link

https://www.scientific.net/AMR.869-870.533.pdf

Reference10 articles.

1. National Bureau of Statistics. National economy and society developed statistical bulletin[M]. Beijing: China Statistics Press, (2012).

2. Yuanshun Du. Regression analysis method for gas daily load [J]. Gas and Heat, 1982, 2(4): 26-30.

3. Shilian Chen, Guiying Gao. Analysis and Forcast for Production and Main Consume Quantity of Energy[J]. SYSTEMS ENGINEERING-THEORY and PRACTICE, 1994, 14(9): 69-73.

4. Contreras J, Espinola R, Nogales F J, et al. ARIMA models to predict next-day electricity prices[J]. Power Systems, IEEE Transactions on, 2003, 18(3): 1014-1020.

5. Zhao Yang, Yan Liu. Application of Neural network in Natural Gas Load Forecasting[J]. Gas and Heat, 2003(6): 331-332.

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