Using Machine Learning to estimate the technical potential of shallow ground-source heat pumps with thermal interference

نویسندگان

چکیده

Abstract The increasing use of ground-source heat pumps (GSHPs) for heating and cooling buildings raises questions regarding the technical potential GSHPs their impact on temperature in shallow subsurface. In this paper, we develop a method using Machine Learning to estimate GSHPs, which enables such an estimation Switzerland with limited data computational resources. A training dataset is constructed based meteorological geological across Switzerland. We analyse correlations importance each input estimating GSHP compare different feature sets models. Random Forest algorithm, trained full dataset, provides best performance potential. resulting model yields R 2 score 0.95 annual energy potential, 0.86 extraction rate, 0.82 number boreholes per system.

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ژورنال

عنوان ژورنال: Journal of Physics: Conference Series

سال: 2021

ISSN: ['1742-6588', '1742-6596']

DOI: https://doi.org/10.1088/1742-6596/2042/1/012010