Machine Learning-Based HT-ATES Simulation | #HTATES #ThermalEnergyStorage #AquiferStorage #EnergyModeling #SustainableHeating #ClimateTech #DataDrivenModeling

Data-Driven Prediction of HT-ATES Temperature Profiles Using Machine Learning and Nearest Neighbor Search

1. Introduction

  • Climate Challenge & Heating Sector: Heating accounts for ~40% of global energy demand and is a major contributor to CO₂ emissions.

  • Role of HT-ATES: High-Temperature Aquifer Thermal Energy Storage (HT-ATES) can reduce fossil fuel use by storing excess heat for use during peak demand.

  • Need for Efficient Modeling: Traditional numerical models (FEM, FDM, FVM) are accurate but computationally intensive (1–10 hours per run), making large-scale integration impractical.


2. Modeling Approaches for HT-ATES

2.1. Numerical Modeling

  • Finite difference, finite volume, and finite element methods simulate fluid and heat flow accurately.

  • Require complex setups and significant runtime.

  • Unsuitable for real-time or large-scale energy system planning.

2.2. Analytical Modeling

  • Only one known analytical method exists.

  • Fast but inaccurate across wide parameter ranges (e.g., changes in screen length, injection rate).

  • Limited use in energy system simulations.

3. Data-Driven Model (DDM) for HT-ATES

3.1. Overview of the DDM

  • Predicts temperature profiles based on 7 key aquifer parameters.

  • Combines Machine Learning (ML) with a Nearest Neighbor Search to ensure efficiency and accuracy.

  • Replaces computational physics simulations with a data-based approximation.

3.2. Role of Machine Learning

  • Extreme Gradient Boosting (XGBoost) is used to predict Recovery Efficiency (RE).

  • Trained on 3501 simulation data points covering diverse parameter ranges.

  • Validated using RMSE; ML outperforms linear interpolation significantly.

3.3. Nearest Neighbor Search for Profile Matching

  • Uses RE, injected temperature, and ambient temperature to find the closest match from the dataset.

  • Ensures physical realism by basing predictions on simulated full-physics results.

3.4. Profile Adjustment

  • Temperature profiles are adjusted to match new injected temperatures.

  • Converted to temperature-over-volume format to generalize across operation profiles.

4. Model Evaluation

4.1. Accuracy Testing

  • ML predicted RE with RMSE of 1.45 percentage points vs. 16 for linear interpolation.

  • DDM overall produces low error when compared with original numerical models.

4.2. Parameter Sensitivity

  • Tested different nearest-neighbor distance metrics (using subsets of parameters).

  • Best performance achieved using RE, injection temperature, and ambient temperature.

4.3. Limitations Identified

  • Accuracy drops when using operation profiles vastly different from the base (e.g., mixed extraction/injection).

  • Some heat loss behavior tied to time is not captured in volume-based profiles.

5. Application Potential and Use Cases

  • Energy System Integration: Enables HT-ATES to be included in broader energy planning tools.

  • Scenario Analysis & Optimization: Fast simulation allows thousands of runs for uncertainty and sensitivity analysis.

  • Sustainable Heating Systems: Helps design systems combining solar/geothermal with seasonal heat storage.

6. Conclusion

  • The proposed DDM enables fast, accurate temperature profile prediction for HT-ATES systems.

  • It fills a critical gap between physics-based modeling and energy system simulation.

  • Open-source implementation makes it ready for adoption in research and planning tools.

#HTATES #ThermalEnergyStorage #AquiferStorage #EnergyModeling #SustainableHeating #ClimateTech #DataDrivenModeling

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