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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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Geothermal Energy |
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| Online Access: | https://doi.org/10.1186/s40517-024-00324-3 |
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| author | Abdulrahman Al-Fakih Abbas Al-khudafi Ardiansyah Koeshidayatullah SanLinn Kaka Abdelrigeeb Al-Gathe |
| author_facet | Abdulrahman Al-Fakih Abbas Al-khudafi Ardiansyah Koeshidayatullah SanLinn Kaka Abdelrigeeb Al-Gathe |
| author_sort | Abdulrahman Al-Fakih |
| collection | DOAJ |
| description | 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 temperature forecasting in Yemen’s western region. The data set, collected from 108 geothermal wells, was divided into two sets: set 1 with 1402 data points and set 2 with 995 data points. Feature engineering prepared the data for model training. We evaluated a suite of machine learning regression models, from simple linear regression (SLR) to multi-layer perceptron (MLP). Hyperparameter tuning using Bayesian optimization (BO) was selected as the optimization process to boost model accuracy and performance. The MLP model outperformed others, achieving high $$\text {R}^{2}$$ R 2 values and low error values across all metrics after BO. Specifically, MLP achieved $$\text {R}^{2}$$ R 2 of 0.999, with MAE of 0.218, RMSE of 0.285, RAE of 4.071%, and RRSE of 4.011%. BO significantly upgraded the Gaussian process model, achieving an $$\text {R}^{2}$$ R 2 of 0.996, a minimum MAE of 0.283, RMSE of 0.575, RAE of 5.453%, and RRSE of 8.717%. The models demonstrated robust generalization capabilities with high $$\text {R}^{2}$$ R 2 values and low error metrics (MAE and RMSE) across all sets. This study highlights the potential of enhanced ML techniques and the novel BO in optimizing geothermal energy resource exploitation, contributing significantly to renewable energy research and development. |
| format | Article |
| id | doaj-art-10a945fb549a4bf0bb68c45c7e571138 |
| institution | Kabale University |
| issn | 2195-9706 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Geothermal Energy |
| spelling | doaj-art-10a945fb549a4bf0bb68c45c7e5711382025-01-12T12:14:25ZengSpringerOpenGeothermal Energy2195-97062025-01-0113112910.1186/s40517-024-00324-3Forecasting geothermal temperature in western Yemen with Bayesian-optimized machine learning regression modelsAbdulrahman Al-Fakih0Abbas Al-khudafi1Ardiansyah Koeshidayatullah2SanLinn Kaka3Abdelrigeeb Al-Gathe4College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & MineralsFaculty of Engineering and IT, Oil, and Gas Engineering Department, Emirates International UniversityCollege of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & MineralsCollege of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & MineralsFaculty of Engineering and Petroleum, Department of Petroleum Engineering, Hadhramout UniversityAbstract 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 temperature forecasting in Yemen’s western region. The data set, collected from 108 geothermal wells, was divided into two sets: set 1 with 1402 data points and set 2 with 995 data points. Feature engineering prepared the data for model training. We evaluated a suite of machine learning regression models, from simple linear regression (SLR) to multi-layer perceptron (MLP). Hyperparameter tuning using Bayesian optimization (BO) was selected as the optimization process to boost model accuracy and performance. The MLP model outperformed others, achieving high $$\text {R}^{2}$$ R 2 values and low error values across all metrics after BO. Specifically, MLP achieved $$\text {R}^{2}$$ R 2 of 0.999, with MAE of 0.218, RMSE of 0.285, RAE of 4.071%, and RRSE of 4.011%. BO significantly upgraded the Gaussian process model, achieving an $$\text {R}^{2}$$ R 2 of 0.996, a minimum MAE of 0.283, RMSE of 0.575, RAE of 5.453%, and RRSE of 8.717%. The models demonstrated robust generalization capabilities with high $$\text {R}^{2}$$ R 2 values and low error metrics (MAE and RMSE) across all sets. This study highlights the potential of enhanced ML techniques and the novel BO in optimizing geothermal energy resource exploitation, contributing significantly to renewable energy research and development.https://doi.org/10.1186/s40517-024-00324-3Machine learningGeothermal temperature forecastingRenewable energyOptimizationYemen |
| spellingShingle | Abdulrahman Al-Fakih Abbas Al-khudafi Ardiansyah Koeshidayatullah SanLinn Kaka Abdelrigeeb Al-Gathe Forecasting geothermal temperature in western Yemen with Bayesian-optimized machine learning regression models Geothermal Energy Machine learning Geothermal temperature forecasting Renewable energy Optimization Yemen |
| title | Forecasting geothermal temperature in western Yemen with Bayesian-optimized machine learning regression models |
| title_full | Forecasting geothermal temperature in western Yemen with Bayesian-optimized machine learning regression models |
| title_fullStr | Forecasting geothermal temperature in western Yemen with Bayesian-optimized machine learning regression models |
| title_full_unstemmed | Forecasting geothermal temperature in western Yemen with Bayesian-optimized machine learning regression models |
| title_short | Forecasting geothermal temperature in western Yemen with Bayesian-optimized machine learning regression models |
| title_sort | forecasting geothermal temperature in western yemen with bayesian optimized machine learning regression models |
| topic | Machine learning Geothermal temperature forecasting Renewable energy Optimization Yemen |
| url | https://doi.org/10.1186/s40517-024-00324-3 |
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