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Sustainable performance enhancement of a heat recovery ground source heat pump system using field data and machine learning
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  • Published: 31 March 2026

Sustainable performance enhancement of a heat recovery ground source heat pump system using field data and machine learning

  • Ying Cui1,
  • Wen Tong Chong1,2,
  • Mahendra Varman1,
  • Xinru Wang3,
  • Taocheng Wan4,
  • Yun Deng5 &
  • …
  • Song Pan4 

Scientific Reports , Article number:  (2026) Cite this article

  • 55 Accesses

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Energy science and technology
  • Engineering

Abstract

To mitigate soil heat accumulation that reduces the energy efficiency of ground source heat pump systems in cooling-dominated regions, a novel heat recovery ground source heat pump (HRGSHP) system is designed and a three-year field test is performed in this paper. The system parallels a conventional condenser and heat recovery condenser within a single heat pump, recovering excess condenser heat to provide 50 °C hot water during summer operation. Results demonstrate that the HRGSHP effectively limits soil temperature rise to ∼0.45 ℃ while meeting the air conditioning demand. To further enhance efficiency, a multi-objective optimization framework combining a genetic algorithm and backpropagation neural network (GA-BPNN) model with a technique for order preference by similarity to ideal solution (TOPSIS) is developed. This enables accurate energy performance prediction and optimal operational parameter setpoint determination. The optimized system achieved improvements, with average coefficients of performance for system (COPs) and heat pump (COPu) increased by 27% and 11% in winter, respectively, the energy efficiency ratios for system (EERs) and heat pump (EERu) increased by 21% and 11% in summer, respectively, and operational costs were reduced by 19%. This work provides experimental evidence and optimization guidelines for implementing HRGSHP systems in building applications.

Data availability

All data generated or analyzed during this study are not publicly available due to confidentiality agreements with the collaborating institution, but are available from the corresponding author on reasonable request.

Abbreviations

GSHP:

Ground source heat pump

HRGSHP:

Ground source heat pump with condensation heat recovery

BPNN:

Back propagation neural network

PCC:

Pearson correlation coefficient

COP:

Coefficient of performance

COPu :

Coefficient of heat pump performance

COPs :

Coefficient of system performance

EER:

Energy efficiency ratio

EERu :

Energy efficiency ratio of heat pump

EERs :

Energy efficiency ratio of system

MAP:

The mean absolute error

MAPE:

The mean absolute percentage error

RMSE:

The root-mean-square error (%)

GHE:

Ground heat exchangers

HPs :

Heat pump units

ML:

Machine learning

Ps :

Power consumption of system (kW)

Tc :

Condensing temperature (°C)

T o,g :

outlet temperature of ground side (°C)

TOPSIS:

Technique for order preference by similarity to ideal solution

T i,g :

Inlet temperature of ground source side (°C)

T i,r :

Intlet temperature of heat recovery side (°C)

T o,r :

Outlet temperature of heat recovery side (°C)

G u :

Flow rate of user side (m3/h)

G g :

Flow rate of ground side (m3/h)

T i,u :

Inlet temperature of user side (°C)

T o,u :

Outlet temperature of user side

T s :

Soil temperature (°C)

t :

Outdoor temperature (°C)

d :

Outdoor humidity (%)

GA:

Genetic algorithm

G r :

Flow rate of heat recovery side (m3/h)

IR:

Improvement rate

PIR:

Performance improvement rates

Pu :

Power consumption of HPs (kW)

Te :

Evaporating temperature (°C)

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Acknowledgements

The work described in this paper was supported by all authors. Special thanks to Guizhou China Tobacco Industry Co., Ltd., Tongren Cigarette Factory, for its data assistance during the preparation of this manuscript.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, 50603, Malaysia

    Ying Cui, Wen Tong Chong & Mahendra Varman

  2. Centre for Energy Sciences, Universiti Malaya, Kuala Lumpur, 50603, Malaysia

    Wen Tong Chong

  3. Department of Building Environment and Energy Application Engineering, Faculty of Mechanical Engineering, Tianjin University of Commerce, Tianjin, 300134, China

    Xinru Wang

  4. Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, 100124, China

    Taocheng Wan & Song Pan

  5. Guizhou China Tobacco Industry Co., Ltd. Tongren Cigarette Factory, Tongren, 554300, China

    Yun Deng

Authors
  1. Ying Cui
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  2. Wen Tong Chong
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  4. Xinru Wang
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Contributions

Y.C.: Funding Acquisition, Conceptualisation, Methodology, Software (MATLAB), Writing (literature review, drafting, validation, and visualization), Data Curation (acquisition, analysis, and interpretation of data). W.T.C., M.V., and X.R.W.: Reviewing, Supervision. T.C.W.: Software (MATLAB). Y.D.: Data curation (acquisition). S.P.: Reviewing.

Corresponding authors

Correspondence to Ying Cui or Mahendra Varman.

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The authors declare no competing interests.

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Cui, Y., Chong, W.T., Varman, M. et al. Sustainable performance enhancement of a heat recovery ground source heat pump system using field data and machine learning. Sci Rep (2026). https://doi.org/10.1038/s41598-026-45353-z

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  • Received: 06 December 2025

  • Accepted: 18 March 2026

  • Published: 31 March 2026

  • DOI: https://doi.org/10.1038/s41598-026-45353-z

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Keywords

  • Ground source heat pump
  • Condensation heat recovery
  • Field experiments
  • Energy performance
  • Optimization
  • Genetic algorithm
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