Physics Informed Neural Networks for Control Oriented Thermal Modeling of Buildings

@article{Gokhale2022PhysicsIN,
  title={Physics Informed Neural Networks for Control Oriented Thermal Modeling of Buildings},
  author={Gargya Gokhale and Bert Claessens and Chris Develder},
  journal={ArXiv},
  year={2022},
  volume={abs/2111.12066}
}

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References

SHOWING 1-10 OF 52 REFERENCES
Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamics
Reinforcement learning for optimal control of low exergy buildings
Experimental Demonstration of Frequency Regulation by Commercial Buildings—Part I: Modeling and Hierarchical Control Design
TLDR
It is demonstrated that commercial buildings can track a frequency regulation signal with high accuracy and minimal occupant discomfort in a realistic environment, and it is shown that buildings can determine the reserve capacity and baseline power a priori, and identify the optimal tradeoff between frequency regulation and energy efficiency.
...
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2
3
4
5
...