Integrated Intelligent Energy ›› 2026, Vol. 48 ›› Issue (4): 27-34.doi: 10.3969/j.issn.2097-0706.2026.04.004

• Integrated Energy System Analysis and Evaluation • Previous Articles     Next Articles

Real-time heat supply identification method and application for rural residences in North and Northeast China during heating season

CHEN Yue1(), TIAN Shen1,*(), ZHAN Binfei2(), SHI Xiaodong3(), XU Weichen1(), HU Kaiyong1()   

  1. 1 Tianjin Key Laboratory of Refrigeration TechnologyTianjin University of CommerceTianjin 300134, China
    2 China Academy of Building ResearchBeijing 100013, China
    3 Tianjin New Technology Industrial Park Great Far East Refrigeration Equipment Engineering Technology Company LimitedTianjin 300110, China
  • Received:2025-01-09 Revised:2025-02-24 Published:2025-06-04
  • Contact: TIAN Shen E-mail:chen_yue1223@163.com;tianshen@tjcu.edu.cn;zhanbf@emcso.com;981729022@qq.com;3385303474@qq.com;hky422@tjcu.edu.cn
  • Supported by:
    Xinjiang Uygur Autonomous Region Key Research and Development Project(2023B02029-1);National College Students' Innovation and Entrepreneurship Training Project(202410069271)

Abstract:

Heating systems in rural residences in cold and extremely cold regions face both economic and environmental challenges. A real-time heat supply identification model for rural residences based on the principle of conservation of energy was proposed. The model comprehensively considered factors such as the instantaneous heat gain and heat transfer of the building envelope, heat variation of indoor air, and air infiltration load. The model was applied to three rural residences in North China and Northeast China, with physical parameters calibrated using measured data from non-heating seasons. After calibration, the model achieved prediction accuracy for instantaneous indoor temperature with a root mean square error, mean bias error, and range normalized root mean square error of below 6.45%,3.31%,and 8.72%, respectively. The calibrated model was used for data analysis of rural residences during heating season. The results showed that the peak heat supply for rural residences in Northeast China differed significantly from that in North China, with a maximum difference of 72.2 W/m2. Additionally, a reduction of 1 ℃ in indoor temperature decreased the heat supply by 12.45% for rural residences in Northeast China and by an average of 12.85% in North China. The findings indicate that predicting real-time heat supply under different indoor temperatures is of great significance for effective heating regulation in rural residences.

Key words: real-time heat supply, identification model, heating load, rural residence, cold and extremely cold regions

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