From Experiment to Deployment: An Integrated Framework for Predictive Modelling and Visualisation of Thermoelectric HVAC Systems

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Abstract
Thermoelectric (TE) heating, ventilation, and air-conditioning (HVAC) systems represent a sustainable alternative to conventional vapor-compression technologies; however, their adoption is limited by relatively low coefficients of performance (COP) and challenges in performance optimization. This study presents an integrated experimental, modelling, and deployment framework for predicting and evaluating TE HVAC performance. A custom-built test rig was developed to generate experimental data under controlled laboratory conditions, capturing key thermodynamic variables. A total of 487 samples were used to benchmark multiple regression and machine learning models. Among the evaluated approaches, Linear Regression achieved the best performance, with a root mean squared error (RMSE) of 3.28 and a coefficient of determination (R²) of 0.885, outperforming more complex models. Beyond statistical evaluation, model predictions were validated through recomputation of heat transfer rate and COP, demonstrating strong thermodynamic consistency and physical interpretability. The validated model was subsequently implemented within a Shiny-based graphical interface, enabling interactive scenario analysis and real-time performance evaluation. The results demonstrate that, for moderate-scale experimental datasets, simple and interpretable models can outperform more complex approaches while maintaining physical consistency. The proposed framework provides a practical pathway for integrating experimental data, predictive modelling, and decision-support tools in thermoelectric HVAC applications.

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