The method combines implicit PINODEs for component dynamics with DAE solvers and a lightweight corrector to simulate large HVAC systems with multi-fold speedups and low errors.
Neural differential equations for temperature control in buildings under demand response programs
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Scalable Physics-Informed Neural Differential Equations and Data-Driven Algorithms for HVAC Systems
The method combines implicit PINODEs for component dynamics with DAE solvers and a lightweight corrector to simulate large HVAC systems with multi-fold speedups and low errors.