IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v144y2018icp243-264.html
   My bibliography  Save this article

Analysis and forecasting of the oil consumption in China based on combination models optimized by artificial intelligence algorithms

Author

Listed:
  • Li, Jingrui
  • Wang, Rui
  • Wang, Jianzhou
  • Li, Yifan

Abstract

Forecasting petroleum consumption is a complicated and challenging task because many parameters affect the oil consumption. Whereas a highly accurate prediction model can help one utilize data resources reasonably, an inaccurate model will lead to a waste of resources. Thus, choosing an optimization model with the best forecasting accuracy is not only a challenging task but also a remarkable problem for oil consumption forecasting. However, a single model cannot always satisfy time series forecasting and the variations in oil consumption. In this paper, a total of 26 combination models using traditional combination method were developed to increase the prediction accuracy and avoid the problem of individual risk prediction methods "over-fitting", which would reduce the accuracy. Our conclusion is that the proposed combination models provide desirable forecasting results compared to the traditional combination model, and the combination method of TCM-NNCT is the most feasible and effective one. This paper also discussed the factors related to the statistical models and the results can be used by policy makers to plan strategies. Numerical results demonstrated that the proposed combined model is not only robust but able to approximate the actual consumption satisfactorily, which is an effective tool in analysis for the energy market.

Suggested Citation

  • Li, Jingrui & Wang, Rui & Wang, Jianzhou & Li, Yifan, 2018. "Analysis and forecasting of the oil consumption in China based on combination models optimized by artificial intelligence algorithms," Energy, Elsevier, vol. 144(C), pages 243-264.
    • Handle: RePEc:eee:energy:v:144:y:2018:i:c:p:243-264
    DOI: 10.1016/j.energy.2017.12.042
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217320625
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2017.12.042?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. Tascikaraoglu, A. & Uzunoglu, M., 2014. "A review of combined approaches for prediction of short-term wind speed and power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 243-254.
    2. Alquist, Ron & Kilian, Lutz & Vigfusson, Robert J., 2013. "Forecasting the Price of Oil," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 427-507, Elsevier.
    3. Alesina, Alberto & Dollar, David, 2000. "Who Gives Foreign Aid to Whom and Why?," Journal of Economic Growth, Springer, vol. 5(1), pages 33-63, March.
    4. Yu, Feng & Xu, Xiaozhong, 2014. "A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network," Applied Energy, Elsevier, vol. 134(C), pages 102-113.
    5. Ziramba, Emmanuel, 2010. "Price and income elasticities of crude oil import demand in South Africa: A cointegration analysis," Energy Policy, Elsevier, vol. 38(12), pages 7844-7849, December.
    6. Azadeh, A. & Khakestani, M. & Saberi, M., 2009. "A flexible fuzzy regression algorithm for forecasting oil consumption estimation," Energy Policy, Elsevier, vol. 37(12), pages 5567-5579, December.
    7. Cunado, Juncal & Perez de Gracia, Fernando, 2003. "Do oil price shocks matter? Evidence for some European countries," Energy Economics, Elsevier, vol. 25(2), pages 137-154, March.
    8. Abadir, Karim M. & Distaso, Walter, 2007. "Testing joint hypotheses when one of the alternatives is one-sided," Journal of Econometrics, Elsevier, vol. 140(2), pages 695-718, October.
    9. Park, Sun-Young & Yoo, Seung-Hoon, 2014. "The dynamics of oil consumption and economic growth in Malaysia," Energy Policy, Elsevier, vol. 66(C), pages 218-223.
    10. Altinay, Galip, 2007. "Short-run and long-run elasticities of import demand for crude oil in Turkey," Energy Policy, Elsevier, vol. 35(11), pages 5829-5835, November.
    11. Cassola, Federico & Burlando, Massimiliano, 2012. "Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output," Applied Energy, Elsevier, vol. 99(C), pages 154-166.
    12. Xiao, Ling & Wang, Jianzhou & Dong, Yao & Wu, Jie, 2015. "Combined forecasting models for wind energy forecasting: A case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 271-288.
    13. Liu, Nian & Tang, Qingfeng & Zhang, Jianhua & Fan, Wei & Liu, Jie, 2014. "A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids," Applied Energy, Elsevier, vol. 129(C), pages 336-345.
    14. Apergis, Nicholas & Loomis, David & Payne, James E., 2010. "Are fluctuations in coal consumption transitory or permanent? Evidence from a panel of US states," Applied Energy, Elsevier, vol. 87(7), pages 2424-2426, July.
    15. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    16. Wang, Jianzhou & Jiang, Haiyan & Zhou, Qingping & Wu, Jie & Qin, Shanshan, 2016. "China’s natural gas production and consumption analysis based on the multicycle Hubbert model and rolling Grey model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1149-1167.
    17. Xiao, Liye & Wang, Jianzhou & Hou, Ru & Wu, Jie, 2015. "A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting," Energy, Elsevier, vol. 82(C), pages 524-549.
    18. Zhao, Weigang & Wang, Jianzhou & Lu, Haiyan, 2014. "Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model," Omega, Elsevier, vol. 45(C), pages 80-91.
    19. Pauwels, Laurent L. & Vasnev, Andrey L., 2016. "A note on the estimation of optimal weights for density forecast combinations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 391-397.
    20. Ghosh, Sajal, 2009. "Import demand of crude oil and economic growth: Evidence from India," Energy Policy, Elsevier, vol. 37(2), pages 699-702, February.
    21. Ene, Seval & Öztürk, Nursel, 2017. "Grey modelling based forecasting system for return flow of end-of-life vehicles," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 155-166.
    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. H. Murat Ertuğrul & B. Oray Güngör & Uğur Soytaş, 2021. "The Effect of the COVID-19 Outbreak on the Turkish Diesel Consumption Volatility Dynamics," Energy RESEARCH LETTERS, Asia-Pacific Applied Economics Association, vol. 1(1), pages 1-4.
    2. Peng Zhang & Xin Ma & Kun She, 2019. "Forecasting Japan’s Solar Energy Consumption Using a Novel Incomplete Gamma Grey Model," Sustainability, MDPI, vol. 11(21), pages 1-23, October.
    3. Peng Zhang & Xin Ma & Kun She, 2019. "A Novel Power-Driven Grey Model with Whale Optimization Algorithm and Its Application in Forecasting the Residential Energy Consumption in China," Complexity, Hindawi, vol. 2019, pages 1-22, November.
    4. Hongwei Wang & Yuansheng Huang & Chong Gao & Yuqing Jiang, 2019. "Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network," Energies, MDPI, vol. 12(16), pages 1-21, August.
    5. Baratsas, Stefanos G. & Niziolek, Alexander M. & Onel, Onur & Matthews, Logan R. & Floudas, Christodoulos A. & Hallermann, Detlef R. & Sorescu, Sorin M. & Pistikopoulos, Efstratios N., 2022. "A novel quantitative forecasting framework in energy with applications in designing energy-intelligent tax policies," Applied Energy, Elsevier, vol. 305(C).
    6. Li, Jingrui & Wang, Jiyang & Li, Zhiwu, 2023. "A novel combined forecasting system based on advanced optimization algorithm - A study on optimal interval prediction of wind speed," Energy, Elsevier, vol. 264(C).
    7. Pan, Xunzhang & Wang, Lining & Dai, Jiaquan & Zhang, Qi & Peng, Tianduo & Chen, Wenying, 2020. "Analysis of China’s oil and gas consumption under different scenarios toward 2050: An integrated modeling," Energy, Elsevier, vol. 195(C).
    8. Wang, Meng & Wang, Wei & Wu, Lifeng, 2022. "Application of a new grey multivariate forecasting model in the forecasting of energy consumption in 7 regions of China," Energy, Elsevier, vol. 243(C).
    9. Güngör, Bekir Oray & Ertuğrul, H. Murat & Soytaş, Uğur, 2021. "Impact of Covid-19 outbreak on Turkish gasoline consumption," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    10. Xiao, Jin & Li, Yuxi & Xie, Ling & Liu, Dunhu & Huang, Jing, 2018. "A hybrid model based on selective ensemble for energy consumption forecasting in China," Energy, Elsevier, vol. 159(C), pages 534-546.
    11. Cen, Zhongpei & Wang, Jun, 2019. "Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer," Energy, Elsevier, vol. 169(C), pages 160-171.
    12. Wang, Jianzhou & Yang, Wendong & Du, Pei & Li, Yifan, 2018. "Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system," Energy, Elsevier, vol. 148(C), pages 59-78.
    13. Li, Jingrui & Wang, Jianzhou & Zhang, Haipeng & Li, Zhiwu, 2022. "An innovative combined model based on multi-objective optimization approach for forecasting short-term wind speed: A case study in China," Renewable Energy, Elsevier, vol. 201(P1), pages 766-779.
    14. Karakurt, Izzet & Aydin, Gokhan, 2023. "Development of regression models to forecast the CO2 emissions from fossil fuels in the BRICS and MINT countries," Energy, Elsevier, vol. 263(PA).
    15. Hassan, Anas M. & Ayoub, M. & Eissa, M. & Musa, T. & Bruining, Hans & Farajzadeh, R., 2019. "Exergy return on exergy investment analysis of natural-polymer (Guar-Arabic gum) enhanced oil recovery process," Energy, Elsevier, vol. 181(C), pages 162-172.
    16. Ebrahimi-Moghadam, Amir & Mohseni-Gharyehsafa, Behnam & Farzaneh-Gord, Mahmood, 2018. "Using artificial neural network and quadratic algorithm for minimizing entropy generation of Al2O3-EG/W nanofluid flow inside parabolic trough solar collector," Renewable Energy, Elsevier, vol. 129(PA), pages 473-485.
    17. Zhu, Zhu & Liao, Qi & Liang, Yongtu & Qiu, Rui & Zhang, ZeZhou & Zhang, Haoran, 2022. "The era of renewables: Infrastructure disposal strategies under market decline of oil products," Energy, Elsevier, vol. 249(C).

    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. Xiao, Liye & Shao, Wei & Liang, Tulu & Wang, Chen, 2016. "A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting," Applied Energy, Elsevier, vol. 167(C), pages 135-153.
    2. Xiao, Liye & Shao, Wei & Wang, Chen & Zhang, Kequan & Lu, Haiyan, 2016. "Research and application of a hybrid model based on multi-objective optimization for electrical load forecasting," Applied Energy, Elsevier, vol. 180(C), pages 213-233.
    3. Yang, Zhongshan & Wang, Jian, 2018. "A combination forecasting approach applied in multistep wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Applied Energy, Elsevier, vol. 230(C), pages 1108-1125.
    4. Song, Jingjing & Wang, Jianzhou & Lu, Haiyan, 2018. "A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 215(C), pages 643-658.
    5. Zonggui Yao & Chen Wang, 2018. "A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting," Sustainability, MDPI, vol. 10(5), pages 1-33, May.
    6. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    7. Costa, Marcelo Azevedo & Ruiz-Cárdenas, Ramiro & Mineti, Leandro Brioschi & Prates, Marcos Oliveira, 2021. "Dynamic time scan forecasting for multi-step wind speed prediction," Renewable Energy, Elsevier, vol. 177(C), pages 584-595.
    8. Fedoseeva, Svetlana & Zeidan, Rodrigo, 2018. "How (a)symmetric is the response of import demand to changes in its determinants? Evidence from European energy imports," Energy Economics, Elsevier, vol. 69(C), pages 379-394.
    9. Liu, Jinqiang & Wang, Xiaoru & Lu, Yun, 2017. "A novel hybrid methodology for short-term wind power forecasting based on adaptive neuro-fuzzy inference system," Renewable Energy, Elsevier, vol. 103(C), pages 620-629.
    10. Li, Sisi & Khan, Sufyan Ullah & Yao, Yao & Chen, George S. & Zhang, Lin & Salim, Ruhul & Huo, Jiaying, 2022. "Estimating the long-run crude oil demand function of China: Some new evidence and policy options," Energy Policy, Elsevier, vol. 170(C).
    11. Ozturk, Ilhan & Arisoy, Ibrahim, 2016. "An estimation of crude oil import demand in Turkey: Evidence from time-varying parameters approach," Energy Policy, Elsevier, vol. 99(C), pages 174-179.
    12. Rajesh Sharma & Pradeep Kautish & D. Suresh Kumar, 2021. "Assessing Dynamism of Crude Oil Demand in Middle-Income Countries of South Asia: A Panel Data Investigation," Global Business Review, International Management Institute, vol. 22(1), pages 169-183, February.
    13. Yousaf Raza, Muhammad & Lin, Boqiang, 2021. "Oil for Pakistan: What are the main factors affecting the oil import?," Energy, Elsevier, vol. 237(C).
    14. Eleyan, Mohammed I.Abu & Çatık, Abdurrahman Nazif & Balcılar, Mehmet & Ballı, Esra, 2021. "Are long-run income and price elasticities of oil demand time-varying? New evidence from BRICS countries," Energy, Elsevier, vol. 229(C).
    15. Mohammad Jaforullah & Alan King, 2015. "is New Zealand's economy vulnerable to world oil market shocks?," Working Papers 1503, University of Otago, Department of Economics, revised Mar 2015.
    16. Li, Wei-Qin & Chang, Li, 2018. "A combination model with variable weight optimization for short-term electrical load forecasting," Energy, Elsevier, vol. 164(C), pages 575-593.
    17. Khalil Jebran & Abdullah & Mahmoud Moustafa Elhabbaq & Arshad Ali, 2017. "Income and Price Elasticities of Crude Oil Demand in Pakistan," Global Business Review, International Management Institute, vol. 18(6), pages 1373-1383, December.
    18. A. Talha Yalta, 2016. "Bootstrap Inference of Level Relationships in the Presence of Serially Correlated Errors: A Large Scale Simulation Study and an Application in Energy Demand," Computational Economics, Springer;Society for Computational Economics, vol. 48(2), pages 339-366, August.
    19. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    20. Dash, Devi Prasad & Sethi, Narayan & Bal, Debi Prasad, 2018. "Is the demand for crude oil inelastic for India? Evidence from structural VAR analysis," Energy Policy, Elsevier, vol. 118(C), pages 552-558.

    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:144:y:2018:i:c:p:243-264. 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.