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

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, refactored 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 analysis.