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Modelling the thermal behaviour of a building facade using deep learning.

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Summary
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Deep learning models offer superior thermal behavior prediction for walls compared to traditional Fourier methods. This advanced approach enhances building energy consumption accuracy.

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Area of Science:

  • Building science
  • Artificial intelligence
  • Thermal engineering

Background:

  • Traditional Fourier models for wall thermal behavior are limited and often inaccurate.
  • Accurate thermal modeling is crucial for predicting building energy consumption.

Purpose of the Study:

  • To develop and evaluate a deep learning model for predicting wall thermal behavior.
  • To compare the performance of the deep learning model against the traditional Fourier method.

Main Methods:

  • A deep learning (connectionist) model was trained using over a year of temperature data from different wall layers.
  • The model's predictions were validated against actual observed temperature measurements.
  • Performance was compared with the established theoretical Fourier model.

Main Results:

  • The deep learning model significantly outperformed the Fourier method in predicting thermal behavior.
  • The connectionist model provided more accurate approximations of real-world energy consumption.

Conclusions:

  • Deep learning offers a substantial improvement over traditional methods for thermal modeling of building enclosures.
  • This approach enables more precise energy consumption predictions and better understanding of building energy dynamics.