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February 5, 2026

Adapting Machine Learning Models for Indoor Temperature Prediction from Kuwait to the French Riviera Climate

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Authors

ASAhmad SedaghatMNMohammad NazififardEFErwin Franquet

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Overview

Adapts machine learning models to predict indoor temperatures in differing climates, highlighting implications for energy efficiency.

Key Points

  • The research aims to adapt machine learning models for accurate indoor temperature prediction in varying climates.
  • Developed and validated machine learning models against experimental data
  • Measured indoor weather conditions and energy consumption in portable cabins
  • Utilized Internet of Things (IoT) for data storage
  • Conducted transient system simulations to model temperature dynamics
  • Examined nineteen regression models for performance comparison
  • The Matern 5/2 Gaussian process regression model performed best for indoor temperature prediction
  • All examined models demonstrated effective performance in both climate conditions
  • Predicted temperature profiles showed similarities between Kuwait and Nice

Cite This Study

Sedaghat et al. (2025) studied this question.

synapsesocial.com/papers/698433c8f1d9ada3c1fb1371https://doi.org/10.1051/e3sconf/202563604001/pdf
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