Walch, A;
Castello, R;
Mohajeri Pour Rayeni, N;
Gudmundsson, A;
Scartezzini, J-L;
(2021)
Using Machine Learning to estimate the technical potential of shallow ground-source heat pumps with thermal interference.
In:
Journal of Physics: Conference Series.
(pp. 012010).
IOP: Lausanne, Switzerland.
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Abstract
The increasing use of ground-source heat pumps (GSHPs) for heating and cooling of buildings raises questions regarding the technical potential of GSHPs and their impact on the temperature in the shallow subsurface. In this paper, we develop a method using Machine Learning to estimate the technical potential of shallow GSHPs, which enables such an estimation for Switzerland with limited data and computational resources. A training dataset is constructed based on meteorological and geological data across Switzerland. We analyse correlations and the importance of each of the input data for estimating the GSHP potential and compare different input feature sets and Machine Learning models. The Random Forest algorithm, trained on the full dataset, provides the best performance to estimate the GSHP potential. The resulting model yields an R2 score of 0.95 for the annual energy potential, 0.86 for the heat extraction rate, and 0.82 for the potential number of boreholes per GSHP system.
| Type: | Proceedings paper |
|---|---|
| Title: | Using Machine Learning to estimate the technical potential of shallow ground-source heat pumps with thermal interference |
| Event: | CISBAT 2021 Carbon-neutral cities - energy efficiency and renewables in the digital era |
| Location: | Lausanne, Switzerland |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1088/1742-6596/2042/1/012010 |
| Publisher version: | http://dx.doi.org/10.1088/1742-6596/2042/1/012010 |
| Language: | English |
| Additional information: | Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10139733 |
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