A physics-constrained deep learning framework for dynamic modeling of vapor compression systems-Reference-Cited by-同舟云学术

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A physics-constrained deep learning framework for dynamic modeling of vapor compression systems

Published:2024-10 Issue: Volume:254 Page:123734
ISSN:1359-4311
Container-title:Applied Thermal Engineering
language:en
Short-container-title:Applied Thermal Engineering

Author:

Ma Jiacheng^ORCID,Dong Yiyun^ORCID,Qiao Hongtao,Laughman Christopher R.

Publisher

Elsevier BV

Reference28 articles.

1. Energy efficiency trends in residential and commercial buildings;Energy,2010

2. Dynamic modeling for vapor compression systems—Part I: Literature review;Rasmussen;HVAC R Res.,2012

3. Recent advances in dynamic modeling of HVAC equipment. Part 1: Equipment modeling;Li;HVAC R Res.,2014

4. C.R. Laughman, H. Qiao, On closure relations for dynamic vapor compression cycle models, in: Proceedings of the American Modelica Conference, 2018, pp. 9–10.

5. Scalable Bayesian optimization for model calibration: Case study on coupled building and HVAC dynamics;Chakrabarty;Energy Build.,2021

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