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Data-driven modeling of residential air source heat pump system for space heating

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Abstract

Air source heat pump systems must operate efficiently during the winter to ensure that energy-saving targets are met and occupants of residential and commercial buildings have acceptable thermal comfort. Investigation on the behavior of air source heat pump systems based on the analysis of real data is important to improve the system performance and attain optimum performance during winter. This will assist governments and industry sectors in formulating energy policies and improving this technology efficiently. Currently, there is no available studies relating the use of data-driven models for improving air source heat pump system. Thus, a large experimental dataset was obtained from four real projects in Bei**g, and a hybrid and general model (ambient temperature –20 °C to + 17 °C) coupling various data-driven models was established. A methodology was developed to assess and detect outliers in the dataset that cause incorrect results for the developed model. After removing the outliers, a regression analysis revealed an excellent agreement between almost 8000 experimental data samples and the predictions, with a coefficient of determination of 0.87. Two scenarios were considered for improving and optimizing the thermal performance of air source heat pump systems using the developed predictive model. A growth of 8.18% was observed in the coefficient of performance compared with that of the real operation when the indoor temperature was 18–20 °C. The total system performance increased by an average of 15.93% when the indoor temperature was set between 18 and 20 °C and the rated capacity increased from 11.5 to 12 kW.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant Number 51638010) and the Innovative Research Group of the National Natural Science Foundation of China (Grant Number 51521005).

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Authors and Affiliations

  1. Department of Building Science, Tsinghua University, Bei**g, 100084, China

    Alireza Zendehboudi, Jiaan Zhao & **anting Li

  2. Department of Energy and Process Engineering, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway

    Alireza Zendehboudi

Authors
  1. Alireza Zendehboudi
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  2. Jiaan Zhao
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  3. **anting Li
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Correspondence to **anting Li.

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Zendehboudi, A., Zhao, J. & Li, X. Data-driven modeling of residential air source heat pump system for space heating. J Therm Anal Calorim 145, 1863–1876 (2021). https://doi.org/10.1007/s10973-021-10750-1

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