IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v198y2022icp695-711.html
   My bibliography  Save this article

Predicting bioenergy power generation structure using a newly developed grey compositional data model: A case study in China

Author

Listed:
  • Zhang, Kai
  • Yin, Kedong
  • Yang, Wendong

Abstract

Accurate short-term prediction of bioenergy power generation structure can optimize the bioenergy structure and help achieve carbon neutrality. However, there are currently few related studies, and most of them present long-term predictions on the development potential of bioenergy, which cannot meet the modeling requirements of predicting structure. Hence, the Fractional-order-accumulation grey Compositional data Model with Particle swarm is proposed in this paper (PFCM (1,1)) for forecasting bioenergy power generation structure. The proposed model satisfies the modeling requirements by introducing the fractional accumulation operator to ensure the prediction accuracy, and constructing the spherical mapping space to reduce the data dimension. The empirical studies prove that the newly developed model performs better than other models, which is successfully employed to predict bioenergy power generation structure of China for 2020–2024. The results show that the share of renewable municipal waste in bioenergy power generation will exceed that of solid biofuels by 2023 and the share of biogas power generation has not changed much. Furthermore, although the total amount of bioenergy power generation in China is growing rapidly, unbalanced development and small share of power are two important challenges.

Suggested Citation

  • Zhang, Kai & Yin, Kedong & Yang, Wendong, 2022. "Predicting bioenergy power generation structure using a newly developed grey compositional data model: A case study in China," Renewable Energy, Elsevier, vol. 198(C), pages 695-711.
    • Handle: RePEc:eee:renene:v:198:y:2022:i:c:p:695-711
    DOI: 10.1016/j.renene.2022.08.050
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148122012174
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2022.08.050?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. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    2. Long, Huiling & Li, Xiaobing & Wang, Hong & Jia, Jingdun, 2013. "Biomass resources and their bioenergy potential estimation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 344-352.
    3. Wang, Huiwen & Liu, Qiang & Mok, Henry M.K. & Fu, Linghui & Tse, Wai Man, 2007. "A hyperspherical transformation forecasting model for compositional data," European Journal of Operational Research, Elsevier, vol. 179(2), pages 459-468, June.
    4. Wu, Wenqing & Ma, Xin & Zeng, Bo & Wang, Yong & Cai, Wei, 2018. "Application of the novel fractional grey model FAGMO(1,1,k) to predict China's nuclear energy consumption," Energy, Elsevier, vol. 165(PB), pages 223-234.
    5. Li, Nu & Wang, Jianliang & Wu, Lifeng & Bentley, Yongmei, 2021. "Predicting monthly natural gas production in China using a novel grey seasonal model with particle swarm optimization," Energy, Elsevier, vol. 215(PA).
    6. Jiang, Xinyuan & Sommer, Sven G. & Christensen, Knud V., 2011. "A review of the biogas industry in China," Energy Policy, Elsevier, vol. 39(10), pages 6073-6081, October.
    7. Chen, Longjian & Xing, Li & Han, Lujia, 2009. "Renewable energy from agro-residues in China: Solid biofuels and biomass briquetting technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2689-2695, December.
    8. Zambujal-Oliveira, João & Mouta-Lopes, Manuel & Bangueses, Ricardo, 2021. "Real options appraisal of forestry investments under information scarcity in biomass markets," Resources Policy, Elsevier, vol. 74(C).
    9. Wu, Lifeng & Liu, Sifeng & Fang, Zhigeng & Xu, Haiyan, 2015. "Properties of the GM(1,1) with fractional order accumulation," Applied Mathematics and Computation, Elsevier, vol. 252(C), pages 287-293.
    10. Luo, Xilin & Duan, Huiming & He, Leiyuhang, 2020. "A Novel Riccati Equation Grey Model And Its Application In Forecasting Clean Energy," Energy, Elsevier, vol. 205(C).
    11. Cui, Can & Shan, Yuli & Liu, Jianghua & Yu, Xiang & Wang, Hongtao & Wang, Zhen, 2019. "CO2 emissions and their spatial patterns of Xinjiang cities in China," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    12. Zhao, Xiaohu & Huang, Guohe & Lu, Chen & Zhou, Xiong & Li, Yongping, 2020. "Impacts of climate change on photovoltaic energy potential: A case study of China," Applied Energy, Elsevier, vol. 280(C).
    13. Szarka, Nora & Scholwin, Frank & Trommler, Marcus & Fabian Jacobi, H. & Eichhorn, Marcus & Ortwein, Andreas & Thrän, Daniela, 2013. "A novel role for bioenergy: A flexible, demand-oriented power supply," Energy, Elsevier, vol. 61(C), pages 18-26.
    14. Wang, Zheng-Xin & Li, Qin & Pei, Ling-Ling, 2018. "A seasonal GM(1,1) model for forecasting the electricity consumption of the primary economic sectors," Energy, Elsevier, vol. 154(C), pages 522-534.
    15. Mallapragada, Dharik S. & Sepulveda, Nestor A. & Jenkins, Jesse D., 2020. "Long-run system value of battery energy storage in future grids with increasing wind and solar generation," Applied Energy, Elsevier, vol. 275(C).
    16. Wu, Chunying & Wang, Jianzhou & Hao, Yan, 2022. "Deterministic and uncertainty crude oil price forecasting based on outlier detection and modified multi-objective optimization algorithm," Resources Policy, Elsevier, vol. 77(C).
    17. Naqvi, Salman Raza & Jamshaid, Sana & Naqvi, Muhammad & Farooq, Wasif & Niazi, Muhammad Bilal Khan & Aman, Zaeem & Zubair, Muhammad & Ali, Majid & Shahbaz, Muhammad & Inayat, Abrar & Afzal, Waheed, 2018. "Potential of biomass for bioenergy in Pakistan based on present case and future perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1247-1258.
    18. Graham, Neal T. & Gakkhar, Nikhil & Singh, Akash Deep & Evans, Meredydd & Stelmach, Tanner & Durga, Siddarth & Godara, Rakesh & Gajera, Bhautik & Wise, Marshall & Sarma, Anil K., 2022. "Integrated analysis of increased bioenergy futures in India," Energy Policy, Elsevier, vol. 168(C).
    19. Yigang Wei & Zhichao Wang & Huiwen Wang & Yan Li & Zhenyu Jiang, 2019. "Predicting population age structures of China, India, and Vietnam by 2030 based on compositional data," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-42, April.
    20. Anca-Couce, A. & Hochenauer, C. & Scharler, R., 2021. "Bioenergy technologies, uses, market and future trends with Austria as a case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    21. Ma, Weimin & Zhu, Xiaoxi & Wang, Miaomiao, 2013. "Forecasting iron ore import and consumption of China using grey model optimized by particle swarm optimization algorithm," Resources Policy, Elsevier, vol. 38(4), pages 613-620.
    22. Bartolini, Andrea & Mazzoni, Stefano & Comodi, Gabriele & Romagnoli, Alessandro, 2021. "Impact of carbon pricing on distributed energy systems planning," Applied Energy, Elsevier, vol. 301(C).
    23. Mandley, S.J. & Daioglou, V. & Junginger, H.M. & van Vuuren, D.P. & Wicke, B., 2020. "EU bioenergy development to 2050," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    24. Dotzauer, Martin & Oehmichen, Katja & Thrän, Daniela & Weber, Christoph, 2022. "Empirical greenhouse gas assessment for flexible bioenergy in interaction with the German power sector," Renewable Energy, Elsevier, vol. 181(C), pages 1100-1109.
    25. Zappa, William & Junginger, Martin & van den Broek, Machteld, 2019. "Is a 100% renewable European power system feasible by 2050?," Applied Energy, Elsevier, vol. 233, pages 1027-1050.
    26. Liu, Tong & Xu, Gang & Cai, Peng & Tian, Longhu & Huang, Qili, 2011. "Development forecast of renewable energy power generation in China and its influence on the GHG control strategy of the country," Renewable Energy, Elsevier, vol. 36(4), pages 1284-1292.
    27. Appiah-Nkansah, Nana Baah & Li, Jun & Rooney, William & Wang, Donghai, 2019. "A review of sweet sorghum as a viable renewable bioenergy crop and its techno-economic analysis," Renewable Energy, Elsevier, vol. 143(C), pages 1121-1132.
    28. Yang, Wendong & Sun, Shaolong & Hao, Yan & Wang, Shouyang, 2022. "A novel machine learning-based electricity price forecasting model based on optimal model selection strategy," Energy, Elsevier, vol. 238(PC).
    29. Dominic Woolf & Johannes Lehmann & David R. Lee, 2016. "Optimal bioenergy power generation for climate change mitigation with or without carbon sequestration," Nature Communications, Nature, vol. 7(1), pages 1-11, December.
    30. Gu, Fu & Wang, Jiqiang & Guo, Jianfeng & Fan, Ying, 2020. "How the supply and demand of steam coal affect the investment in clean energy industry? Evidence from China," Resources Policy, Elsevier, vol. 69(C).
    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. Chen, Yan & Lifeng, Wu & Lianyi, Liu & Kai, Zhang, 2020. "Fractional Hausdorff grey model and its properties," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    2. Copp, David A. & Nguyen, Tu A. & Byrne, Raymond H. & Chalamala, Babu R., 2022. "Optimal sizing of distributed energy resources for planning 100% renewable electric power systems," Energy, Elsevier, vol. 239(PE).
    3. Jåstad, Eirik Ogner & Bolkesjø, Torjus Folsland & Trømborg, Erik & Rørstad, Per Kristian, 2020. "The role of woody biomass for reduction of fossil GHG emissions in the future North European energy sector," Applied Energy, Elsevier, vol. 274(C).
    4. Yi-Chung Hu, 2021. "Forecasting tourism demand using fractional grey prediction models with Fourier series," Annals of Operations Research, Springer, vol. 300(2), pages 467-491, May.
    5. Wang, Zheng-Xin & Wang, Zhi-Wei & Li, Qin, 2020. "Forecasting the industrial solar energy consumption using a novel seasonal GM(1,1) model with dynamic seasonal adjustment factors," Energy, Elsevier, vol. 200(C).
    6. Kang, Yating & Yang, Qing & Bartocci, Pietro & Wei, Hongjian & Liu, Sylvia Shuhan & Wu, Zhujuan & Zhou, Hewen & Yang, Haiping & Fantozzi, Francesco & Chen, Hanping, 2020. "Bioenergy in China: Evaluation of domestic biomass resources and the associated greenhouse gas mitigation potentials," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    7. Bao, Chao & Fang, Chuang-lin, 2013. "Geographical and environmental perspectives for the sustainable development of renewable energy in urbanizing China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 464-474.
    8. Şahin, Utkucan & Ballı, Serkan & Chen, Yan, 2021. "Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods," Applied Energy, Elsevier, vol. 302(C).
    9. Zhou, Wenhao & Li, Hailin & Zhang, Zhiwei, 2022. "A novel seasonal fractional grey model for predicting electricity demand: A case study of Zhejiang in China," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 200(C), pages 128-147.
    10. He, Xinbo & Wang, Yong & Zhang, Yuyang & Ma, Xin & Wu, Wenqing & Zhang, Lei, 2022. "A novel structure adaptive new information priority discrete grey prediction model and its application in renewable energy generation forecasting," Applied Energy, Elsevier, vol. 325(C).
    11. Xie, Xuemei & Liu, Xiaojie & Blanco, Cristina, 2023. "Evaluating and forecasting the niche fitness of regional innovation ecosystems: A comparative evaluation of different optimized grey models," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    12. Xin Zhang & Yun-Ze Li & Ao-Bing Wang & Li-Jun Gao & Hui-Juan Xu & Xian-Wen Ning, 2020. "The Development Strategies and Technology Roadmap of Bioenergy for a Typical Region: A Case Study in the Beijing-Tianjin-Hebei Region in China," Energies, MDPI, vol. 13(4), pages 1-25, February.
    13. Fabián Almonacid, 2018. "Bioenergy in an Agroforestry Economy under Crisis: Complement and Conflict. La Araucanía, Chile, 1990–2016," Sustainability, MDPI, vol. 10(12), pages 1-19, November.
    14. Dotzauer, Martin & Oehmichen, Katja & Thrän, Daniela & Weber, Christoph, 2022. "Empirical greenhouse gas assessment for flexible bioenergy in interaction with the German power sector," Renewable Energy, Elsevier, vol. 181(C), pages 1100-1109.
    15. Algieri, Angelo & Andiloro, Serafina & Tamburino, Vincenzo & Zema, Demetrio Antonio, 2019. "The potential of agricultural residues for energy production in Calabria (Southern Italy)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 1-14.
    16. 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).
    17. Mei Sang & Jing Jiang & Xin Huang & Feifei Zhu & Qian Wang, 2024. "Spatial and temporal changes in population distribution and population projection at county level in China," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    18. Zhai, Jihua & Burke, Ian T. & Stewart, Douglas I., 2021. "Beneficial management of biomass combustion ashes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    19. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    20. Tian, Wenjing & Li, Jianhao & Zhu, Lirong & Li, Wen & He, Linyan & Gu, Li & Deng, Rui & Shi, Dezhi & Chai, Hongxiang & Gao, Meng, 2021. "Insights of enhancing methane production under high-solid anaerobic digestion of wheat straw by calcium peroxide pretreatment and zero valent iron addition," Renewable Energy, Elsevier, vol. 177(C), pages 1321-1332.

    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:renene:v:198:y:2022:i:c:p:695-711. 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/renewable-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.