IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v36y2011i9p5460-5465.html
   My bibliography  Save this article

Energy resources demand-supply system analysis and empirical research based on non-linear approach

Author

Listed:
  • Sun, Mei
  • Wang, Xiaofang
  • Chen, Ying
  • Tian, Lixin

Abstract

The three-dimensional energy resources demand-supply system is established for two regions of China, in which determination of unknown parameters is significant for the real energy resources demand-supply system. In this paper, based on Shanghai’s energy statistic data (1999–2005), the parameters of the energy resources demand-supply system can be identified by using the neural network method, and an energy resources demand-supply system reflecting the real energy resources demand-supply relationship in Shanghai is obtained. The dynamical behavior of this system is also analyzed. Theoretical analysis and numerical simulation indicate that the obtained energy resources demand-supply system has the capacity of self-regulation and demonstrates a steady state. Finally, to ensure the steady development of the energy resources demand-supply system, corresponding measures are proposed.

Suggested Citation

  • Sun, Mei & Wang, Xiaofang & Chen, Ying & Tian, Lixin, 2011. "Energy resources demand-supply system analysis and empirical research based on non-linear approach," Energy, Elsevier, vol. 36(9), pages 5460-5465.
  • Handle: RePEc:eee:energy:v:36:y:2011:i:9:p:5460-5465
    DOI: 10.1016/j.energy.2011.07.036
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544211004841
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2011.07.036?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yadaiah, N. & Sivakumar, L. & Deekshatulu, B.L., 2000. "Parameter identification via neural networks with fast convergence," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 51(3), pages 157-167.
    2. Chedid, R. & Kobrosly, M. & Ghajar, R., 2007. "A supply model for crude oil and natural gas in the Middle East," Energy Policy, Elsevier, vol. 35(4), pages 2096-2109, April.
    3. Askari, Hossein & Krichene, Noureddine, 2010. "An oil demand and supply model incorporating monetary policy," Energy, Elsevier, vol. 35(5), pages 2013-2021.
    4. Azadeh, A. & Asadzadeh, S.M. & Ghanbari, A., 2010. "An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: Uncertain and complex environments," Energy Policy, Elsevier, vol. 38(3), pages 1529-1536, March.
    5. Ito, Toshihide & Chen, Youqing & Ito, Shoichi & Yamaguchi, Kaoru, 2010. "Prospect of the upper limit of the energy demand in China from regional aspects," Energy, Elsevier, vol. 35(12), pages 5320-5327.
    6. Krichene, Noureddine, 2002. "World crude oil and natural gas: a demand and supply model," Energy Economics, Elsevier, vol. 24(6), pages 557-576, November.
    7. Kanamura, Takashi, 2009. "A supply and demand based volatility model for energy prices," Energy Economics, Elsevier, vol. 31(5), pages 736-747, September.
    8. Chai, Qimin & Zhang, Xiliang, 2010. "Technologies and policies for the transition to a sustainable energy system in china," Energy, Elsevier, vol. 35(10), pages 3995-4002.
    9. Geem, Zong Woo & Roper, William E., 2009. "Energy demand estimation of South Korea using artificial neural network," Energy Policy, Elsevier, vol. 37(10), pages 4049-4054, October.
    10. Jamasb, Tooraj & Pollitt, Michael, 2008. "Security of supply and regulation of energy networks," Energy Policy, Elsevier, vol. 36(12), pages 4584-4589, December.
    11. Zhang, Ming & Mu, Hailin & Li, Gang & Ning, Yadong, 2009. "Forecasting the transport energy demand based on PLSR method in China," Energy, Elsevier, vol. 34(9), pages 1396-1400.
    12. Murat, Yetis Sazi & Ceylan, Halim, 2006. "Use of artificial neural networks for transport energy demand modeling," Energy Policy, Elsevier, vol. 34(17), pages 3165-3172, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fang, Guochang & Tian, Lixin & Fu, Min & Sun, Mei, 2013. "The impacts of carbon tax on energy intensity and economic growth – A dynamic evolution analysis on the case of China," Applied Energy, Elsevier, vol. 110(C), pages 17-28.
    2. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
    3. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    4. Zhu, Jianhua & Peng, Yan & Gong, Zhuping & Sun, Yanming & Lai, Chaoan & Wang, Qing & Zhu, Xiaojun & Gan, Zhongxue, 2019. "Dynamic analysis of SNG and PNG supply: The stability and robustness view #," Energy, Elsevier, vol. 185(C), pages 717-729.
    5. Mir Hossein Mousavi, 2015. "An Estimation of Natural Gas Demand in Household Sector of Iran; the Structural Time Series Approach," Proceedings of International Academic Conferences 2804383, International Institute of Social and Economic Sciences.
    6. Zhang, Wenbin & Tian, Lixin & Wang, Minggang & Zhen, Zaili & Fang, Guochang, 2016. "The evolution model of electricity market on the stable development in China and its dynamic analysis," Energy, Elsevier, vol. 114(C), pages 344-359.
    7. Piotr Razniak & Slawomir Dorocki & Tomasz Rachwal & Anna Winiarczyk-Razniak, 2021. "Influence of Energy Sector Corporations on the Corporate Control Functions of Cities," International Journal of Energy Economics and Policy, Econjournals, vol. 11(2), pages 333-340.
    8. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    9. Yang, Honglin & Wang, Lin & Tian, Lixin, 2015. "Evolution of competition in energy alternative pathway and the influence of energy policy on economic growth," Energy, Elsevier, vol. 88(C), pages 223-233.
    10. Fang, Guochang & Tian, Lixin & Sun, Mei & Fu, Min, 2012. "Analysis and application of a novel three-dimensional energy-saving and emission-reduction dynamic evolution system," Energy, Elsevier, vol. 40(1), pages 291-299.
    11. Piotr Raźniak & Sławomir Dorocki & Tomasz Rachwał & Anna Winiarczyk-Raźniak, 2021. "The Role of the Energy Sector in the Command and Control Function of Cities in Conditions of Sustainability Transitions," Energies, MDPI, vol. 14(22), pages 1-14, November.
    12. Fang, Guochang & Tian, Lixin & Fu, Min & Sun, Mei, 2014. "Government control or low carbon lifestyle? – Analysis and application of a novel selective-constrained energy-saving and emission-reduction dynamic evolution system," Energy Policy, Elsevier, vol. 68(C), pages 498-507.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yu, Shiwei & Wei, Yi-Ming & Wang, Ke, 2012. "A PSO–GA optimal model to estimate primary energy demand of China," Energy Policy, Elsevier, vol. 42(C), pages 329-340.
    2. Behrang, M.A. & Assareh, E. & Ghalambaz, M. & Assari, M.R. & Noghrehabadi, A.R., 2011. "Forecasting future oil demand in Iran using GSA (Gravitational Search Algorithm)," Energy, Elsevier, vol. 36(9), pages 5649-5654.
    3. Geem, Zong Woo, 2011. "Transport energy demand modeling of South Korea using artificial neural network," Energy Policy, Elsevier, vol. 39(8), pages 4644-4650, August.
    4. Sun, Mei & Zhang, Pei-Pei & Shan, Tian-Hua & Fang, Cui-Cui & Wang, Xiao-Fang & Tian, Li-Xin, 2012. "Research on the evolution model of an energy supply–demand network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(19), pages 4506-4516.
    5. Yu, Shi-wei & Zhu, Ke-jun, 2012. "A hybrid procedure for energy demand forecasting in China," Energy, Elsevier, vol. 37(1), pages 396-404.
    6. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    7. Muhammad Muhitur Rahman & Syed Masiur Rahman & Md Shafiullah & Md Arif Hasan & Uneb Gazder & Abdullah Al Mamun & Umer Mansoor & Mohammad Tamim Kashifi & Omer Reshi & Md Arifuzzaman & Md Kamrul Islam &, 2022. "Energy Demand of the Road Transport Sector of Saudi Arabia—Application of a Causality-Based Machine Learning Model to Ensure Sustainable Environment," Sustainability, MDPI, vol. 14(23), pages 1-21, December.
    8. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    9. Wei Sun & Yujun He & Hong Chang, 2015. "Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model," Energies, MDPI, vol. 8(2), pages 1-21, January.
    10. Satrio Mukti Wibowo & Dedi Budiman Hakim & Baba Barus & Akhmad Fauzi, 2022. "Estimation of Energy Demand in Indonesia using Artificial Neural Network," International Journal of Energy Economics and Policy, Econjournals, vol. 12(6), pages 261-271, November.
    11. Okullo, Samuel J. & Reynès, Frédéric, 2011. "Can reserve additions in mature crude oil provinces attenuate peak oil?," Energy, Elsevier, vol. 36(9), pages 5755-5764.
    12. Yousaf Raza, Muhammad & Lin, Boqiang, 2021. "Oil for Pakistan: What are the main factors affecting the oil import?," Energy, Elsevier, vol. 237(C).
    13. Assareh, E. & Behrang, M.A. & Assari, M.R. & Ghanbarzadeh, A., 2010. "Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran," Energy, Elsevier, vol. 35(12), pages 5223-5229.
    14. Taran Fæhn, Cathrine Hagem, Lars Lindholt, Ståle Mæland, and Knut Einar Rosendahl, 2017. "Climate policies in a fossil fuel producing country demand versus supply side policies," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    15. Marianne Haug, 2011. "Clean energy and international oil," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 27(1), pages 92-116, Spring.
    16. Herrerias, M.J. & Liu, G., 2013. "Electricity intensity across Chinese provinces: New evidence on convergence and threshold effects," Energy Economics, Elsevier, vol. 36(C), pages 268-276.
    17. Tehreem Fatima & Enjun Xia & Muhammad Ahad, 2019. "Oil demand forecasting for China: a fresh evidence from structural time series analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(3), pages 1205-1224, June.
    18. Llorca, Manuel & Baños, José & Somoza, José & Arbués, Pelayo, 2014. "A latent class approach for estimating energy demands and efficiency in transport: An application to Latin America and the Caribbean," Efficiency Series Papers 2014/04, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    19. Reza Hafezi & Amir Naser Akhavan & Mazdak Zamani & Saeed Pakseresht & Shahaboddin Shamshirband, 2019. "Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand," Energies, MDPI, vol. 12(21), pages 1-22, October.
    20. Kankal, Murat & AkpInar, Adem & Kömürcü, Murat Ihsan & Özsahin, Talat Sükrü, 2011. "Modeling and forecasting of Turkey's energy consumption using socio-economic and demographic variables," Applied Energy, Elsevier, vol. 88(5), pages 1927-1939, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:36:y:2011:i:9:p:5460-5465. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.