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<p>To address the low energy efficiency, high operating costs, and limited research on dynamic collaborative optimization of air-source and ground-source coupled heat pump systems, this study investigates a typical office building in Handan. It proposes a collaborative optimization scheme of operation strategy and capacity configuration. The original system’s energy consumption characteristics were identified through field measurements and validated by a TRNSYS model. Based on dynamic load characteristics across different heating stages, a staged load distribution strategy was proposed. A regression experimental design was adopted, with GSHP load ratio as the independent variable and system energy consumption as the objective, to determine the optimal parameter combination via quadratic fitting. Measured results confirm low energy efficiency of the original system, and the model validation error is below 6%. The optimal strategy dictates that in early and late heating periods, the GSHP bears 75% of the load and the ASHP bears 25%; in the middle heating period, three GSHPs are prioritized as the base load, supplemented by the ASHP. Compared to the original system, the optimized configuration significantly reduces total heating season energy consumption (by approximately 2.8%), increases comprehensive energy efficiency by about 7.5%, and achieves considerable economic and environmental benefits. This study highlights the innovations of staged load distribution and regression-based collaborative optimization, providing a reference for similar systems.</p>

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Collaborative Optimization of Capacity and Operation Strategy for an Air-Source and Ground-Source Heat Pump Coupled Heating System

  • 期刊论文

  • 发表日期：

    2026年7月4日

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  • Huan Liu,
  • Junqing Liu,
  • Peng Li,
  • Yanru Lu,
  • Xiaojie Niu,
  • Jinghui Luo,
  • Yongchao Mu

摘要

To address the low energy efficiency, high operating costs, and limited research on dynamic collaborative optimization of air-source and ground-source coupled heat pump systems, this study investigates a typical office building in Handan. It proposes a collaborative optimization scheme of operation strategy and capacity configuration. The original system’s energy consumption characteristics were identified through field measurements and validated by a TRNSYS model. Based on dynamic load characteristics across different heating stages, a staged load distribution strategy was proposed. A regression experimental design was adopted, with GSHP load ratio as the independent variable and system energy consumption as the objective, to determine the optimal parameter combination via quadratic fitting. Measured results confirm low energy efficiency of the original system, and the model validation error is below 6%. The optimal strategy dictates that in early and late heating periods, the GSHP bears 75% of the load and the ASHP bears 25%; in the middle heating period, three GSHPs are prioritized as the base load, supplemented by the ASHP. Compared to the original system, the optimized configuration significantly reduces total heating season energy consumption (by approximately 2.8%), increases comprehensive energy efficiency by about 7.5%, and achieves considerable economic and environmental benefits. This study highlights the innovations of staged load distribution and regression-based collaborative optimization, providing a reference for similar systems.