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

Gongyi Li, Tao Luo (), Jianghua Xiong, Yanna Gao, Xi Meng, Yaoguo Zuo, Yi Liu, Jing Ma, Qiuwen Chen, Yuxin Liu, Yichong Xin and Yangjie Ye
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Gongyi Li: College of Architecture and Urban Planning, Qingdao University of Technology, Qingdao 266033, 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

Agriculture, 2025, vol. 15, issue 2, 1-17

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.

Keywords: forecasting model; rural energy survey; case study; consumption amount; consumption rate (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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