Forecasting geothermal temperature in western Yemen with Bayesian-optimized machine learning regression models
Abstract Geothermal energy is a sustainable resource for power generation, particularly in Yemen. Efficient utilization necessitates accurate forecasting of subsurface temperatures, which is challenging with conventional methods. This research leverages machine learning (ML) to optimize geothermal t...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-01-01
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| Series: | Geothermal Energy |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40517-024-00324-3 |
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