An Intelligent Regression-Based Approach for Predicting a Geothermal Heat Exchanger’s Behavior in a Bioclimatic House Context
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An Intelligent Regression-Based Approach for Predicting a Geothermal Heat Exchanger’s Behavior in a Bioclimatic House ContextAuthor(s)
Date
2024-05Citation
Díaz-Longueira, A.; Rubiños, M.; Arcano-Bea, P.; Calvo-Rolle, J.L.; Quintián, H.; Zayas-Gato, F. An Intelligent Regression-Based Approach for Predicting a Geothermal Heat Exchanger’s Behavior in a Bioclimatic House Context. Energies 2024, 17, 2706. https://doi.org/10.3390/en17112706
Abstract
[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
Geothermal heat exchanger
Prediction
Random forest
SVR
MLP
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Rights
Creative Commons Attribution (CC BY) license
https://creativecommons.org/licenses/by/4.0/
ISSN
1996-1073