Optimizing Sustainable Power Generation with Triplet Deep Borehole Heat Exchangers: A Machine Learning Approach

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Geothermal energy, a renewable and sustainable resource, has significant potential for meeting global energy demands; most of the study on production generation relies numerical simulation. However, computational intensity physics-based simulations geothermal poses challenges. This explores integration machine learning models with simulation to forecast long-term electricity from triplet deep borehole heat exchanger system. A large dataset generated through COMSOL Multiphysics served as input three models: Decision Tree, XGBoost, Random Forest. The Forest model outperformed others, achieving lowest error metrics Root Mean Square Percentage Error (RMSPE) 0.104, Absolute (MAPE) 0.0539, highest R² value 0.9996. These indicate that RF provides exceptional prediction accuracy generalization capabilities. combined approach significantly reduced time required, enabling forecasting an additional 15 years power using Forest, which makes it easier faster than waiting almost 21 hours before simulating 25 years. results confirm viability optimizing forecasting, ensuring sustainability operational efficiency in generation.

Language: Английский

Optimizing integration strategies for biomass gasification with natural gas pyrolysis under a low-carbon hydrogen enhancement approach: A financial and environmental perspective

Chemical Engineering Science, Journal Year: 2025, Volume and Issue: unknown, P. 121654 - 121654

Published: April 1, 2025

Language: Английский