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Enhanced data-driven method for design cooling load calculation averting the overestimation due to design weather data

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  • Li, Qiyan
  • Chen, Youming
  • Dong, Kaijun
  • Sun, Qin

Abstract

Design cooling load is always overestimated when using design weather data (DWD), resulting in oversized air-conditioning (AC) system, low operating energy efficiency and highly initial investment. In this study, a data-driven model is developed based on explainable feature selection (EFS) and multi-task learning (MTL), termed as the EFS-MTL model, for averting the overestimation. Design cooling loads under various non-guarantee rates (such as 0.4 %, 1 %, 2 % and 50 h) were calculated using heat balance method for a large number of sample rooms combined by building/room parameters with multi-year hourly-recorded weather data (HWD) of a city. The EFS-MTL model was then trained by the dataset of the sample rooms with the room parameters (as inputs) and the corresponding design cooling loads (as outputs). To illustrate the EFS-MTL model's capacity in eliminating the overestimation, the EFS-MTL model was trained with the datasets created by HWD of Beijing, Changsha and Guangzhou. The design cooling loads calculated by the EFS-MTL model and DWD were compared. Results show that the median relative deviations of the EFS-MTL model range from −1.44 % to 0.27 %. These results demonstrate that the EFS-MTL model provides an effective approach to correctly and fast calculate design cooling load of AC systems without DWD.

Suggested Citation

  • Li, Qiyan & Chen, Youming & Dong, Kaijun & Sun, Qin, 2025. "Enhanced data-driven method for design cooling load calculation averting the overestimation due to design weather data," Energy, Elsevier, vol. 337(C).
    • Handle: RePEc:eee:energy:v:337:y:2025:i:c:s0360544225042975
    DOI: 10.1016/j.energy.2025.138655
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    References listed on IDEAS

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