IDEAS home Printed from https://ideas.repec.org/a/wly/jjmath/v2022y2022i1n8443619.html

A Novel Fractional Hausdorff Discrete Grey Model for Forecasting the Renewable Energy Consumption

Author

Listed:
  • Yuzhen Chen
  • Suzhen Li
  • Shuangbing Guo

Abstract

Reducing carbon dioxide emissions and using renewable energy to replace fossil fuels have become an essential trend in future energy development. Renewable energy consumption has a significant impact on energy security so accurate prediction of renewable energy consumption can help the energy department formulate relevant policies and adjust the energy structure. Based on this, a novel Fractional Hausdorff Discrete Grey Model, abbreviated FHDGM (1,1), is developed in this study. The paper investigates the model’s characteristics. The fractional‐order r of the FHDGM (1,1) model is optimized using particle swarm optimization. Subsequently, through two empirical analyses, the prediction accuracy of the FHDGM (1,1) model is proven to be higher than that of other models. Finally, the proposed model is applied with a view to forecasting the consumption of renewable energy for the years 2021 to 2023 in three different areas: the Asia Pacific region, Europe, and the world. The study’s findings will offer crucial forecasting data for worldwide energy conservation and emission reduction initiatives.

Suggested Citation

  • Yuzhen Chen & Suzhen Li & Shuangbing Guo, 2022. "A Novel Fractional Hausdorff Discrete Grey Model for Forecasting the Renewable Energy Consumption," Journal of Mathematics, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:jjmath:v:2022:y:2022:i:1:n:8443619
    DOI: 10.1155/2022/8443619
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/2022/8443619
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/8443619?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
    ---><---

    References listed on IDEAS

    as
    1. Meenal, R. & Selvakumar, A. Immanuel, 2018. "Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters," Renewable Energy, Elsevier, vol. 121(C), pages 324-343.
    2. Xiong, Pingping & Li, Kailing & Shu, Hui & Wang, Junjie, 2021. "Forecast of natural gas consumption in the Asia-Pacific region using a fractional-order incomplete gamma grey model," Energy, Elsevier, vol. 237(C).
    3. Tong, Mingyu & Dong, Jingrong & Luo, Xilin & Yin, Dejun & Duan, Huiming, 2022. "Coal consumption forecasting using an optimized grey model: The case of the world's top three coal consumers," Energy, Elsevier, vol. 242(C).
    4. Majid Dehghani & Hossein Riahi-Madvar & Farhad Hooshyaripor & Amir Mosavi & Shahaboddin Shamshirband & Edmundas Kazimieras Zavadskas & Kwok-wing Chau, 2019. "Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 12(2), pages 1-20, January.
    5. Jamil, Rehan, 2020. "Hydroelectricity consumption forecast for Pakistan using ARIMA modeling and supply-demand analysis for the year 2030," Renewable Energy, Elsevier, vol. 154(C), pages 1-10.
    6. Chao Liu, 2022. "Empirical Analysis of the Relationship between Renewable Energy Consumption and Economic Growth Based on the Grey Markov Model," Journal of Mathematics, John Wiley & Sons, vol. 2022(1).
    7. Chao Liu & Miaochao Chen, 2022. "Empirical Analysis of the Relationship between Renewable Energy Consumption and Economic Growth Based on the Grey Markov Model," Journal of Mathematics, Hindawi, vol. 2022, pages 1-11, March.
    Full references (including those not matched with items on IDEAS)

    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. Wang, Yong & Yang, Zhongsen & Zhou, Ying & Liu, Hao & Yang, Rui & Sun, Lang & Sapnken, Flavian Emmanuel & Narayanan, Govindasami, 2025. "A novel structure adaptive new information priority grey Bernoulli model and its application in China's renewable energy production," Renewable Energy, Elsevier, vol. 239(C).
    2. Yijue Sun & Fenglin Zhang, 2022. "Grey Multivariable Prediction Model of Energy Consumption with Different Fractional Orders," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    3. Marius Dalian Doran & Maria Magdalena Poenaru & Alexandra Lucia Zaharia & Sorana Vătavu & Oana Ramona Lobonț, 2022. "Fiscal Policy, Growth, Financial Development and Renewable Energy in Romania: An Autoregressive Distributed Lag Model with Evidence for Growth Hypothesis," Energies, MDPI, vol. 16(1), pages 1-18, December.
    4. Jiaxin Yu & Jun Wang, 2020. "Optimization Design of a Rain-Power Utilization System Based on a Siphon and Its Application in a High-Rise Building," Energies, MDPI, vol. 13(18), pages 1-18, September.
    5. Zhang, Meng & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2022. "A novel flexible grey multivariable model and its application in forecasting energy consumption in China," Energy, Elsevier, vol. 239(PE).
    6. Gao, Yuan & Hu, Zehuan & Chen, Wei-An & Liu, Mingzhe & Ruan, Yingjun, 2025. "A revolutionary neural network architecture with interpretability and flexibility based on Kolmogorov–Arnold for solar radiation and temperature forecasting," Applied Energy, Elsevier, vol. 378(PA).
    7. Zhang, Jia & Ge, Yadong & Wang, Yibo & Tao, Junyu & Li, Zaixin & Fu, Shuang & Wang, Xingcan & Zhong, Yuzhen & Yan, Beibei & Chen, Guanyi, 2025. "Photovoltaic power plants in mountainous area: Environmental impacts analysis based on random forest algorithm," Renewable Energy, Elsevier, vol. 254(C).
    8. Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    9. Nourani, Vahid & Sharghi, Elnaz & Behfar, Nazanin & Zhang, Yongqiang, 2022. "Multi-step-ahead solar irradiance modeling employing multi-frequency deep learning models and climatic data," Applied Energy, Elsevier, vol. 315(C).
    10. Gul, Eid & Baldinelli, Giorgio & Bartocci, Pietro & Shamim, Tariq & Domenighini, Piergiovanni & Cotana, Franco & Wang, Jinwen & Fantozzi, Francesco & Bianchi, Francesco, 2023. "Transition toward net zero emissions - Integration and optimization of renewable energy sources: Solar, hydro, and biomass with the local grid station in central Italy," Renewable Energy, Elsevier, vol. 207(C), pages 672-686.
    11. Zhou, Chenyu & Shen, Yun & Wu, Haixin & Wang, Jianhong, 2022. "Using fractional discrete Verhulst model to forecast Fujian's electricity consumption in China," Energy, Elsevier, vol. 255(C).
    12. Kaloop, Mosbeh R. & Bardhan, Abidhan & Kardani, Navid & Samui, Pijush & Hu, Jong Wan & Ramzy, Ahmed, 2021. "Novel application of adaptive swarm intelligence techniques coupled with adaptive network-based fuzzy inference system in predicting photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    13. Yi Liu & Zhiqiang Jiang & Zhongkai Feng & Yuyun Chen & Hairong Zhang & Ping Chen, 2019. "Optimization of Energy Storage Operation Chart of Cascade Reservoirs with Multi-Year Regulating Reservoir," Energies, MDPI, vol. 12(20), pages 1-20, October.
    14. Mohammad Ehteram & Ali Najah Ahmed & Chow Ming Fai & Haitham Abdulmohsin Afan & Ahmed El-Shafie, 2019. "Accuracy Enhancement for Zone Mapping of a Solar Radiation Forecasting Based Multi-Objective Model for Better Management of the Generation of Renewable Energy," Energies, MDPI, vol. 12(14), pages 1-26, July.
    15. Prince Waqas Khan & Yung-Cheol Byun & Sang-Joon Lee & Namje Park, 2020. "Machine Learning Based Hybrid System for Imputation and Efficient Energy Demand Forecasting," Energies, MDPI, vol. 13(11), pages 1-23, May.
    16. Marcos Geraldo Gomes & Victor Hugo Carlquist da Silva & Luiz Fernando Rodrigues Pinto & Plinio Centoamore & Salvatore Digiesi & Francesco Facchini & Geraldo Cardoso de Oliveira Neto, 2020. "Economic, Environmental and Social Gains of the Implementation of Artificial Intelligence at Dam Operations toward Industry 4.0 Principles," Sustainability, MDPI, vol. 12(9), pages 1-19, April.
    17. Xing Zhang & Zhuoqun Wei, 2019. "A Hybrid Model Based on Principal Component Analysis, Wavelet Transform, and Extreme Learning Machine Optimized by Bat Algorithm for Daily Solar Radiation Forecasting," Sustainability, MDPI, vol. 11(15), pages 1-20, July.
    18. Xu, Zhicun & Xie, Naiming & Diao, Huakang, 2023. "Lithium-ion battery state of health monitoring based on an adaptive variable fractional order multivariate grey model," Energy, Elsevier, vol. 283(C).
    19. Yagli, Gokhan Mert & Yang, Dazhi & Srinivasan, Dipti, 2019. "Automatic hourly solar forecasting using machine learning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 487-498.
    20. Liu, Shuihan & Wei, Yunjie & Peng, Pan & Wang, Shouyang, 2025. "Joint interval forecasting of renewable energy stocks using a secondary decomposition approach," Renewable Energy, Elsevier, vol. 245(C).

    More about this item

    Statistics

    Access and download statistics

    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:wly:jjmath:v:2022:y:2022:i:1:n:8443619. 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: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/1469 .

    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.