Machine learning enhancement of thermal response tests for geothermal potential evaluations at site and regional scales

Paul Bourhis, Benoît Cousin*, Alessandro F. Rotta Loria, Lyesse Laloui

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This study explores and validates a machine learning approach for the practical, effective, and precise prediction of the thermo-physical characteristics that are essential for the analysis and design of shallow geothermal systems, including borehole heat exchangers: (i) undisturbed ground temperature, (ii) ground effective thermal conductivity, and (iii) borehole thermal resistance. Benefiting from 174 thermal response tests from central and western Switzerland, the algorithm is used to provide accurate site-specific as well as regional-scale predictions of the investigated thermo-physical characteristics, which in turn can serve preliminary yet representative evaluations of the geothermal potential of even very broad areas.

Original languageEnglish (US)
Article number102132
JournalGeothermics
Volume95
DOIs
StatePublished - Sep 2021

Keywords

  • Borehole heat exchangers
  • Data-driven predictions
  • Geothermal energy
  • Machine learning
  • Thermal response testing

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Geotechnical Engineering and Engineering Geology
  • Geology

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