An Intelligent Regression-Based Approach for Predicting a Geothermal Heat Exchanger’s Behavior in a Bioclimatic House Context

Antonio Díaz-Longueira, Manuel Rubiños, Paula Arcano-Bea, Jose Luis Calvo-Rolle (), Héctor Quintián () and Francisco Zayas-Gato
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Antonio Díaz-Longueira: CTC Research Group, University of A Coruña, Calle Mendizábal s/n, 15403 Ferrol, Spain
Manuel Rubiños: CTC Research Group, University of A Coruña, Calle Mendizábal s/n, 15403 Ferrol, Spain
Paula Arcano-Bea: CTC Research Group, University of A Coruña, Calle Mendizábal s/n, 15403 Ferrol, Spain
Jose Luis Calvo-Rolle: CTC Research Group, University of A Coruña, Calle Mendizábal s/n, 15403 Ferrol, Spain
Héctor Quintián: CTC Research Group, University of A Coruña, Calle Mendizábal s/n, 15403 Ferrol, Spain
Francisco Zayas-Gato: CTC Research Group, University of A Coruña, Calle Mendizábal s/n, 15403 Ferrol, Spain

Energies, 2024, vol. 17, issue 11, 1-15

Abstract: Growing dependence on fossil fuels is one of the critical factors accelerating climate change, a global concern that can destabilize ecosystems and economies worldwide. In this context, renewable energy is emerging as a sustainable and environmentally responsible alternative. Among the options, geothermal energy stands out for its ability to provide heat and electricity consistently and efficiently, offering a feasible solution to reduce the carbon footprint and promote more sustainable development in a globalized economy. In this work, a machine learning approach is proposed to predict the behavior of a horizontal heat exchanger from a bioclimatic house. First, a correlation analysis was conducted for optimal feature selection. Then, several regression techniques were applied to predict the output temperature of the geothermal exchanger. Satisfactory prediction results were obtained in different scenarios over the whole dataset. Also, a significant correlation between several sensors was concluded.

Keywords: energy efficiency; geothermal heat exchanger; prediction; random forest; SVR; MLP (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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