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
Physically Consistent Neural Networks for building thermal modeling: theory and analysis
TLDR
This work presents a novel physics-informed NN architecture, dubbed Physically Consistent NN (PCNN), which only requires past operational data and no engineering overhead, including prior knowledge in a linear module running in parallel to a classical NN.
Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamics
Optimal temperature control of intermittently heated buildings using Model Predictive Control: Part I – Building modeling
Reinforcement learning for optimal control of low exergy buildings
Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC
Data-driven Predictive Control for Unlocking Building Energy Flexibility: A Review
Building modeling as a crucial part for building predictive control
Deep reinforcement learning to optimise indoor temperature control and heating energy consumption in buildings
Model predictive control strategy applied to different types of building for space heating
Multi-floor building heating models in MATLAB and Modelica environments
...
...