TR2024-140

Integrating Generative Machine Learning Models and Physics-Based Models for Building Energy Simulation

    •  Vanfretti, L., Laughman, C.R., Chakrabarty, A., "Integrating Generative Machine Learning Models and Physics-Based Models for Building Energy Simulation", American Modelica Conference, October 2024.
      BibTeX TR2024-140 PDF
      • @inproceedings{Vanfretti2024oct,
      • author = {Vanfretti, Luigi and Laughman, Christopher R. and Chakrabarty, Ankush}},
      • title = {Integrating Generative Machine Learning Models and Physics-Based Models for Building Energy Simulation},
      • booktitle = {American Modelica Conference},
      • year = 2024,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2024-140}
      • }
  • MERL Contacts:
  • Research Areas:

    Machine Learning, Multi-Physical Modeling, Optimization

Abstract:

This paper describes the integration of generative deep learning models for data-driven building energy simulation. The generative models (GMs) are trained to learn distributions of building input signals from data using Python and PyTorch and interfaced with physics-based Modelica models. The developed integration requirements provide background on typical needs that focus on building energy simulation performance. Simulation examples using models from the Buildings library, re- factored to receive GM inputs, are presented to illustrate the benefits of the proposed integration approach and how GMs can be used for building energy performance analy- sis.