China's primary energy demands in 2020: Predictions from an MPSO-RBF estimation model

Shiwei Yu, Yi-Ming Wei and Ke Wang

No 15, CEEP-BIT Working Papers from Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology

Abstract: In the present study, a Mix-encoding Particle Swarm Optimization and Radial Basis Function (MPSO-RBF) network-based energy demand forecasting model is proposed and appliedto forecast China's energy consumption until 2020. The energy demand isanalyzed for the period from 1980 to 2009 based on GDP, population, proportion of industry in GDP, urbanization rate, and share of coal energy. The results reveal that the proposed MPSO-RBF based model has fewer hidden nodes andsmaller estimated errors compared with other ANN-based estimation models. The average annual growth of China's energy demand will be 6.70%, 2.81%, and 5.08% for the period between 2010 and 2020 in three scenarios and could reach 6.25 billion, 4.16 billion, and 5.29 billion tons coal equivalentin 2020.Regardless of future scenarios, China's energy efficiency in 2020 will increase by more than 30% compared with 2009.

Keywords: China's energy demand; forecasting; Radial Basis Function (RBF) neural network; energy intensity (search for similar items in EconPapers)
JEL-codes: Q41 Q47 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2011-03
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Published in Energy Conversion and Management, 2012, 61:59-66.

Downloads: (external link)
http://ceep.bit.edu.cn/docs/2018-10/20181011134518426873.pdf (application/pdf)
Our link check indicates that this URL is bad, the error code is: 404 Not Found (http://ceep.bit.edu.cn/docs/2018-10/20181011134518426873.pdf [302 Found]--> https://ceep.bit.edu.cn/docs/2018-10/20181011134518426873.pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:biw:wpaper:15

Access Statistics for this paper

More papers in CEEP-BIT Working Papers from Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology Contact information at EDIRC.
Bibliographic data for series maintained by Zhi-Fu Mi ().