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Prediction of the total day-round thermal load for residential buildings at various scales based on weather forecast data

Author

Listed:
  • Chi, Fang'ai
  • Xu, Liming
  • Pan, Jiajie
  • Wang, Ruonan
  • Tao, Yekang
  • Guo, Yuang
  • Peng, Changhai

Abstract

Up to now, the storage technology of electric energy is not advanced enough, resulting in the excessive electricity usually to be wasted. Conversely, power off could emerge during the peak hours of electricity use, due to inadequate electric generation in the power plant. Therefore, the mathematical models, used to predict the heating and cooling loads in buildings, are necessary to be created. However, there is a considerable amount of energy consumed in the China’s residential buildings, whose heating and cooling loads are characterized by climate sensitivity. In this work, the residential buildings situated in China were regarded as the study buildings. The principal research contents are shown as follows: (1) by creating a digital meshing system for China, the hierarchy system of residential building area at country scale was established; (2) via inputting air temperature data into the digital meshing system, the hierarchy system of air temperature was created; (3) the quantitative correlation between air temperature and thermal load intensity of the residential building was explored, to construct the hierarchy system of thermal load intensity; (4) Combining the above three hierarchy systems, the mesh-based mathematical models for predicting the total day-round heating and cooling load in the near future at country scale was created. The mesh-based mathematical models is expected to perform a scientific evaluation for the heating and cooling load of the residential buildings, aiming at reducing energy waste by planning and preparing the right amount of energy supply in advance.

Suggested Citation

  • Chi, Fang'ai & Xu, Liming & Pan, Jiajie & Wang, Ruonan & Tao, Yekang & Guo, Yuang & Peng, Changhai, 2020. "Prediction of the total day-round thermal load for residential buildings at various scales based on weather forecast data," Applied Energy, Elsevier, vol. 280(C).
    • Handle: RePEc:eee:appene:v:280:y:2020:i:c:s0306261920314471
    DOI: 10.1016/j.apenergy.2020.116002
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    References listed on IDEAS

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