IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i14p3832-d248133.html
   My bibliography  Save this article

A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China

Author

Listed:
  • Pingping Xiong

    (College of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Jia Shi

    (College of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Lingling Pei

    (School of Business Administration, Zhejiang University of Finance and Economics, Hangzhou 310018, China)

  • Song Ding

    (School of Economics, Zhejiang University of Finance and Economics, Hangzhou 310018, China)

Abstract

Haze is the greatest challenge facing China’s sustainable development, and it seriously affects China’s economy, society, ecology and human health. Based on the uncertainty and suddenness of haze, this paper proposes a novel linear time-varying grey model (GM)(1,N) based on interval grey number sequences. Because the original GM(1,N) model based on interval grey number sequences has constant parameters, it neglects the dynamic change characteristics of parameters over time. Therefore, this novel linear time-varying GM(1,N) model, based on interval grey number sequences, is established on the basis of the original GM(1,N) model by introducing a linear time polynomial. To verify the validity and practicability of this model, this paper selects the data of PM 10 , SO 2 and NO 2 concentrations in Beijing, China, from 2008 to 2018, to establish a linear time-varying GM(1,3) model based on interval grey number sequences, and the prediction results are compared with the original GM(1,3) model. The result indicates that the prediction effect of the novel model is better than that of the original model. Finally, this model is applied to forecast PM 10 concentration for 2019 to 2021 in Beijing, and the forecast is made to provide a reference for the government to carry out haze control.

Suggested Citation

  • Pingping Xiong & Jia Shi & Lingling Pei & Song Ding, 2019. "A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China," Sustainability, MDPI, vol. 11(14), pages 1-14, July.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:14:p:3832-:d:248133
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/14/3832/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/14/3832/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zeng, Bo & Li, Chuan, 2016. "Forecasting the natural gas demand in China using a self-adapting intelligent grey model," Energy, Elsevier, vol. 112(C), pages 810-825.
    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. Zhou, Weijie & Wu, Xiaoli & Ding, Song & Pan, Jiao, 2020. "Application of a novel discrete grey model for forecasting natural gas consumption: A case study of Jiangsu Province in China," Energy, Elsevier, vol. 200(C).
    2. Hsin-Yi Yang & Sheng-Kung Chen & Jiun-Shiuan Wang & Chih-Jen Lu & Hung-Yu Lai, 2020. "Farmland Trace Metal Contamination and Management Model—Model Development and a Case Study in Central Taiwan," Sustainability, MDPI, vol. 12(23), pages 1-19, December.
    3. Pruethsan Sutthichaimethee & Sthianrapab Naluang, 2019. "The Efficiency of the Sustainable Development Policy for Energy Consumption under Environmental Law in Thailand: Adapting the SEM-VARIMAX Model," Energies, MDPI, vol. 12(16), pages 1-21, August.

    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. Rao, Congjun & Zhang, Yue & Wen, Jianghui & Xiao, Xinping & Goh, Mark, 2023. "Energy demand forecasting in China: A support vector regression-compositional data second exponential smoothing model," Energy, Elsevier, vol. 263(PC).
    2. Li, Shoujun & Ma, Xiaoping & Yang, Chunyu, 2018. "A combined thermal power plant investment decision-making model based on intelligent fuzzy grey model and ito stochastic process and its application," Energy, Elsevier, vol. 159(C), pages 1102-1117.
    3. Deyun Wang & Yanling Liu & Zeng Wu & Hongxue Fu & Yong Shi & Haixiang Guo, 2018. "Scenario Analysis of Natural Gas Consumption in China Based on Wavelet Neural Network Optimized by Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 11(4), pages 1-16, April.
    4. Zhou, Zhongbing & Qin, Quande, 2020. "Decoding China's natural gas development: A critical discourse analysis of the five-year plans," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    5. Chen, Yan & Lifeng, Wu & Lianyi, Liu & Kai, Zhang, 2020. "Fractional Hausdorff grey model and its properties," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    6. Soltanisarvestani, A. & Safavi, A.A., 2021. "Modeling unaccounted-for gas among residential natural gas consumers using a comprehensive fuzzy cognitive map," Utilities Policy, Elsevier, vol. 72(C).
    7. Duan, Huiming & Pang, Xinyu, 2021. "A multivariate grey prediction model based on energy logistic equation and its application in energy prediction in China," Energy, Elsevier, vol. 229(C).
    8. Huang, Jian-Bai & Luo, Yu-Mei & Feng, Chao, 2019. "An overview of carbon dioxide emissions from China's ferrous metal industry: 1991-2030," Resources Policy, Elsevier, vol. 62(C), pages 541-549.
    9. Beyca, Omer Faruk & Ervural, Beyzanur Cayir & Tatoglu, Ekrem & Ozuyar, Pinar Gokcin & Zaim, Selim, 2019. "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, Elsevier, vol. 80(C), pages 937-949.
    10. Ravnik, J. & Hriberšek, M., 2019. "A method for natural gas forecasting and preliminary allocation based on unique standard natural gas consumption profiles," Energy, Elsevier, vol. 180(C), pages 149-162.
    11. Ding, Song, 2018. "A novel self-adapting intelligent grey model for forecasting China's natural-gas demand," Energy, Elsevier, vol. 162(C), pages 393-407.
    12. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "China's dependency on foreign oil will exceed 80% by 2030: Developing a novel NMGM-ARIMA to forecast China's foreign oil dependence from two dimensions," Energy, Elsevier, vol. 163(C), pages 151-167.
    13. Weiwei Pan & Lirong Jian & Tao Liu, 2019. "Grey system theory trends from 1991 to 2018: a bibliometric analysis and visualization," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1407-1434, December.
    14. Muhammad Ikram, 2021. "Models for Predicting Non-Renewable Energy Competing with Renewable Source for Sustainable Energy Development: Case of Asia and Oceania Region," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 22(2), pages 133-160, December.
    15. Huiming Duan & Xinping Xiao, 2019. "A Multimode Dynamic Short-Term Traffic Flow Grey Prediction Model of High-Dimension Tensors," Complexity, Hindawi, vol. 2019, pages 1-18, June.
    16. Yukseltan, Ergun & Yucekaya, Ahmet & Bilge, Ayse Humeyra & Agca Aktunc, Esra, 2021. "Forecasting models for daily natural gas consumption considering periodic variations and demand segregation," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    17. Bartłomiej Gaweł & Andrzej Paliński, 2021. "Long-Term Natural Gas Consumption Forecasting Based on Analog Method and Fuzzy Decision Tree," Energies, MDPI, vol. 14(16), pages 1-26, August.
    18. Chen, Ying & Koch, Thorsten & Zakiyeva, Nazgul & Zhu, Bangzhu, 2020. "Modeling and forecasting the dynamics of the natural gas transmission network in Germany with the demand and supply balance constraint," Applied Energy, Elsevier, vol. 278(C).
    19. Xunpeng, Shi & Variam, Hari Malamakkavu Padinjare & Tao, Jacqueline, 2017. "Global impact of uncertainties in China’s gas market," Energy Policy, Elsevier, vol. 104(C), pages 382-394.
    20. 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.

    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:gam:jsusta:v:11:y:2019:i:14:p:3832-:d:248133. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.