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Analysis of Dynamic Biogas Consumption in Chinese Rural Areas at Village, Township, and County Levels

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  • Gongyi Li

    (College of Architecture and Urban Planning, Qingdao University of Technology, Qingdao 266033, China
    Biogas Institute of Ministry of Agriculture (BIOMA), Chengdu 610041, China)

  • Tao Luo

    (Biogas Institute of Ministry of Agriculture (BIOMA), Chengdu 610041, China)

  • Jianghua Xiong

    (Rural Energy and Environment Agency of Jiangxi Province, Nanchang 335000, China)

  • Yanna Gao

    (College of Architecture and Urban Planning, Qingdao University of Technology, Qingdao 266033, China)

  • Xi Meng

    (College of Architecture and Urban Planning, Qingdao University of Technology, Qingdao 266033, China)

  • Yaoguo Zuo

    (College of Architecture and Urban Planning, Qingdao University of Technology, Qingdao 266033, China)

  • Yi Liu

    (Biogas Institute of Ministry of Agriculture (BIOMA), Chengdu 610041, China)

  • Jing Ma

    (Rural Energy and Environment Agency of Jiangxi Province, Nanchang 335000, China)

  • Qiuwen Chen

    (Biogas Institute of Ministry of Agriculture (BIOMA), Chengdu 610041, China)

  • Yuxin Liu

    (Rural Energy and Environment Agency of Jiangxi Province, Nanchang 335000, China)

  • Yichong Xin

    (Rural Energy and Environment Agency of Jiangxi Province, Nanchang 335000, China)

  • Yangjie Ye

    (Biogas Institute of Ministry of Agriculture (BIOMA), Chengdu 610041, China)

Abstract

Understanding the characteristics of biogas demand in rural areas is essential for on-demand biogas production and fossil fuel offsetting. However, the spatiotemporal features of rural household energy consumption are unclear. This paper developed a rural biogas demand forecasting model (RBDM) based on the hourly loads of different energy types in rural China. The model requires only a small amount of publicly available input data. The model was verified using household energy survey data collected from five Chinese provinces and one year’s data from a village-scale biogas plant. The results showed that the predicted and measured biogas consumption and dynamic load were consistent. The relative error of village biogas consumption was 11.45%, and the dynamic load showed seasonal fluctuations. Seasonal correction factors were incorporated to improve the model’s accuracy and practicality. The accuracy of the RBDM was 19.27% higher than that of a static energy prediction model. Future research should verify the model using additional cases to guide the design of accurate biogas production and distribution systems.

Suggested Citation

  • Gongyi Li & Tao Luo & Jianghua Xiong & Yanna Gao & Xi Meng & Yaoguo Zuo & Yi Liu & Jing Ma & Qiuwen Chen & Yuxin Liu & Yichong Xin & Yangjie Ye, 2025. "Analysis of Dynamic Biogas Consumption in Chinese Rural Areas at Village, Township, and County Levels," Agriculture, MDPI, vol. 15(2), pages 1-17, January.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:2:p:149-:d:1565041
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    References listed on IDEAS

    as
    1. Lisiak-Zielińska, Marta & Jałoszyńska, Sylwia & Borowiak, Klaudia & Budka, Anna & Dach, Jacek, 2023. "Perception of biogas plants: A public awareness and preference - A case study for the agricultural landscape," Renewable Energy, Elsevier, vol. 217(C).
    2. 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.
    3. González, Aldo & Lagos, Vicente, 2021. "Do LPG prices react to the entry of natural gas? Implications for competition policy," Energy Policy, Elsevier, vol. 152(C).
    4. Park, Joungho & Kang, Sungho & Kim, Sunwoo & Kim, Hana & Kim, Sang-Kyung & Lee, Jay H., 2024. "Optimizing green hydrogen systems: Balancing economic viability and reliability in the face of supply-demand volatility," Applied Energy, Elsevier, vol. 368(C).
    5. Korberg, Andrei David & Skov, Iva Ridjan & Mathiesen, Brian Vad, 2020. "The role of biogas and biogas-derived fuels in a 100% renewable energy system in Denmark," Energy, Elsevier, vol. 199(C).
    6. Luo, Tao & Pan, Junting & Fu, Lintao & Mei, Zili & Kong, Cuixue & Huang, Hailong, 2017. "Reducing biogas emissions from village-scale plant with optimal floating-drum biogas storage tank and operation parameters," Applied Energy, Elsevier, vol. 208(C), pages 312-318.
    7. Cao, Andi & Su, Mengying & Li, Houjian, 2024. "Digitizing the green revolution: E-commerce as a catalyst for clean energy transition in rural China," Energy Economics, Elsevier, vol. 137(C).
    8. Liu, Tingting & Ferrari, Giovanni & Pezzuolo, Andrea & Alengebawy, Ahmed & Jin, Keda & Yang, Gaozhong & Li, Qiang & Ai, Ping, 2023. "Evaluation and analysis of biogas potential from agricultural waste in Hubei Province, China," Agricultural Systems, Elsevier, vol. 205(C).
    9. Yin, Peng & Xie, Jingchao & Ji, Ying & Liu, Jiaping & Hou, Qixian & Zhao, Shanshan & Jing, Pengfei, 2023. "Winter indoor thermal environment and heating demand of low-quality centrally heated houses in cold climates," Applied Energy, Elsevier, vol. 331(C).
    10. Ma, Xiaowei & Li, Chuandong & Kang, Qi & Chen, Danni & Sun, Qingyu, 2024. "Rural household nonagricultural income and energy transition: Evidence from central China," Energy Policy, Elsevier, vol. 188(C).
    11. Ma, Wanglin & Zhou, Xiaoshi & Renwick, Alan, 2019. "Impact of off-farm income on household energy expenditures in China: Implications for rural energy transition," Energy Policy, Elsevier, vol. 127(C), pages 248-258.
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