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June 4, 2026Indoor AirOpen Access

Benchmarking Classical Machine Learning and Hybrid Quantum Approaches for Indoor Thermal Sensation Prediction

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Authors

AAAli Berkay Avcı

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Overview

Randomized trial evaluates thermal sensation prediction in naturally ventilated environments, suggesting a need for better modeling strategies.

Key Points

  • This study aims to evaluate the effectiveness of different models in predicting thermal sensation votes based on environmental and personal variables.
  • Utilized a dataset of 19,291 observations from naturally ventilated environments.
  • Benchmarking of 14 regression models with fivefold cross-validation and hyperparameter optimization.
  • Developed a hybrid quantum-classical model for comparison under identical conditions.
  • Ensemble-based classical models, particularly tuned XGBoost, achieved the highest predictive performance (R2: 0.386, RMSE: 0.983).
  • The hybrid quantum model showed lower performance (R2: 0.246, RMSE: 1.085) and early convergence.
  • Feature ablation analysis indicated personal variables were most significant, while derived thermal indicators added no extra benefit.

Cite This Study

Ali Berkay Avcı (2026) studied this question.

synapsesocial.com/papers/6a211781d499ed480b17058ehttps://doi.org/10.1155/ina/3385686
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