Next Article in Journal
Using Submarine Heat Pumps for Efficient Gas Production from Seabed Hydrate Reservoirs
Previous Article in Journal
Optimization of Agitation and Aeration for Very High Gravity Ethanol Fermentation from Sweet Sorghum Juice by Saccharomyces cerevisiae Using an Orthogonal Array Design
Previous Article in Special Issue
Modeling of Turbine Cycles Using a Neuro-Fuzzy Based Approach to Predict Turbine-Generator Output for Nuclear Power Plants
Energies 2012, 5(3), 577-598; doi:10.3390/en5030577
Article

Demand Forecast of Petroleum Product Consumption in the Chinese Transportation Industry

1,2,3,* , 1
,
2
 and
3
Received: 29 January 2012 / Revised: 18 February 2012 / Accepted: 23 February 2012 / Published: 1 March 2012
(This article belongs to the Special Issue Intelligent Energy Demand Forecasting)
Download PDF [337 KB, uploaded 17 March 2015]

Abstract

In this paper, petroleum product (mainly petrol and diesel) consumption in the transportation sector of China is analyzed. This was based on the Bayesian linear regression theory and Markov Chain Monte Carlo method (MCMC), establishing a demand-forecast model of petrol and diesel consumption introduced into the analytical framework with explanatory variables of urbanization level, per capita GDP, turnover of passengers (freight) in aggregate (TPA, TFA), and civilian vehicle number (CVN) and explained variables of petrol and diesel consumption. Furthermore, we forecast the future consumer demand for oil products during “The 12th Five Year Plan” (2011–2015) based on the historical data covering from 1985 to 2009, finding that urbanization is the most sensitive factor, with a strong marginal effect on petrol and diesel consumption in this sector. From the viewpoint of prediction interval value, urbanization expresses the lower limit of the predicted results, and CVN the upper limit of the predicted results. Predicted value from other independent variables is in the range of predicted values which display a validation range and reference standard being much more credible for policy makers. Finally, a comparison between the predicted results from autoregressive integrated moving average models (ARIMA) and others is made to assess our task.
Keywords: petroleum products consumption; transportation sector; Bayesian linear regression; Markov Chain Monte Carlo method petroleum products consumption; transportation sector; Bayesian linear regression; Markov Chain Monte Carlo method
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote
MDPI and ACS Style

Chai, J.; Wang, S.; Wang, S.; Guo, J. Demand Forecast of Petroleum Product Consumption in the Chinese Transportation Industry. Energies 2012, 5, 577-598.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here

Comments

Cited By

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert