Advanced Computational Models for Thermal System Optimization Using Machine Learning and Hybrid Techniques

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This research demonstrates improved predictive accuracy in thermal system modeling using machine learning, highlighting hybrid optimization methods.

Hybrid models enhanced prediction accuracy compared to traditional ANN-based techniques.
Using metrics like Mean Absolute Error (MAE) and R² highlights substantial performance improvements.
The proposed methodology includes data preprocessing, feature selection, and training hybrid machine learning algorithms.
Findings indicate that integrating advanced machine learning techniques can effectively address traditional modeling shortcomings.

Kathiravan et al. (2025) studied this question.

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This research demonstrates improved predictive accuracy in thermal system modeling using machine learning, highlighting hybrid optimization methods.