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Robust identification of volumetric heat capacity and analysis of thermal response tests by Bayesian inference with correlated residuals

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  • Pasquier, Philippe
  • Marcotte, Denis

Abstract

Bayesian inference has tremendous potential for thermal response test analysis, as it provides uncertainty metrics that are useful for the design of ground-coupled heat pump systems. The inference process is computationally heavy and has so far been limited to a few thermal parameters and under the unrealistic assumption of residuals’ independence. In this work, a new closed-form expression of the likelihood and an improved artificial neural network are used to speed up Bayesian inference and consider the strong temporal correlation of the residuals. This efficient strategy allowed the robust inference of the joint distribution of five parameters. Using data measured during a real test of 168 h, this work shows that it is possible to robustly identify the volumetric heat capacity of the ground and grout with an uncertainty of 16.3 and 13.8%, a significant improvement. For the specific data used, it is shown that with independence assumption, some parameters are clearly unrealistic, a problem not encountered when the correlation of the residuals is considered. The impact of the interpretation model, of the test duration and of the sampling frequency was also assessed and illustrated by the sizing of a ground heat exchanger. Results reveal that joint identification of some thermal parameters cannot be achieved reliably by the finite line source model, that duration of thermal response tests should be at least 72 h to avoid large uncertainties on the parameters, and that recording temperature every 2 min degrades the identification of the volumetric heat capacity.

Suggested Citation

  • Pasquier, Philippe & Marcotte, Denis, 2020. "Robust identification of volumetric heat capacity and analysis of thermal response tests by Bayesian inference with correlated residuals," Applied Energy, Elsevier, vol. 261(C).
  • Handle: RePEc:eee:appene:v:261:y:2020:i:c:s0306261919320811
    DOI: 10.1016/j.apenergy.2019.114394
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    Cited by:

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    2. Zhang, Xueping & Han, Zongwei & Ji, Qiang & Zhang, Hongzhi & Li, Xiuming, 2021. "Thermal response tests for the identification of soil thermal parameters: A review," Renewable Energy, Elsevier, vol. 173(C), pages 1123-1135.
    3. Choi, Wonjun & Kikumoto, Hideki & Ooka, Ryozo, 2022. "Probabilistic uncertainty quantification of borehole thermal resistance in real-world scenarios," Energy, Elsevier, vol. 254(PC).
    4. Zhang, Xueping & Han, Zongwei & Meng, Xinwei & Li, Gui & Ji, Qiang & Li, Xiuming & Yang, Lingyan, 2021. "Study on high-precision identification method of ground thermal properties based on neural network model," Renewable Energy, Elsevier, vol. 163(C), pages 1838-1848.
    5. Zhang, Xueping & Han, Zongwei & Li, Gui & Li, Xiuming, 2022. "Effect of temperature measurement error on parameters estimation accuracy for thermal response tests," Renewable Energy, Elsevier, vol. 185(C), pages 230-240.
    6. Beaudry, Gabrielle & Pasquier, Philippe & Marcotte, Denis, 2021. "A fast convolution-based method to simulate time-varying flow rates in closed-loop and standing column well ground heat exchangers," Renewable Energy, Elsevier, vol. 174(C), pages 55-72.
    7. Gigot, Valériane & Francois, Bertrand & Huysmans, Marijke & Gerard, Pierre, 2023. "Monitoring of the thermal plume around a thermally activated borehole heat exchanger and characterization of the ground hydro-geothermal parameters," Renewable Energy, Elsevier, vol. 218(C).
    8. Žnidarič, Luka & Nusev, Gjorgji & Morel, Bertrand & Mougin, Julie & Juričić, Đani & Boškoski, Pavle, 2021. "Evaluating uncertainties in electrochemical impedance spectra of solid oxide fuel cells," Applied Energy, Elsevier, vol. 298(C).

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