Building thermal modeling and model predictive control with physically consistent deep learning for decarbonization and energy optimization

Abstract

Being a primary contributor to global energy consumption and energy-related carbon emissions, the building and building construction sectors are a crucial player in the decarbonization and energy efficiency efforts. This article proposes a novel physically consistent deep learning (PCDL) approach for building thermal modeling and assesses its potential for optimizing building energy efficiency and indoor thermal comfort through model predictive control (MPC). The PCDL model considers physical relationships between system inputs and outputs and is applied to predict the indoor thermal climate. With the strict guarantee of satisfying the laws of physics, the proposed PCDL model has better generalization ability than other machine learning approaches. Subsequently, the PCDL model is integrated into an MPC controller to optimize building energy consumption and indoor thermal comfort. The proposed approach is tested on Carpenter Hall, a multi-zone office building located in Cornell University Campus, through simulations. Based on the simulation results, the PCDL model demonstrate a much better generalization ability for yielding physically-feasible predictions compared to the long short-term memory (LSTM) model. Compared to an On/Off controller, a state space model-based MPC controller, and an LSTM-based MPC controller, the proposed PCDL-based MPC reduces by 5.8%, 4.5%, and 8.9% energy consumption and improves by 55%, 59%, and 64% indoor thermal comfort, respectively, and therefore enhances the building decarbonization progress. The results emphasize the importance of considering physics information in data-driven models and highlight the advantages of the proposed PCDL-based MPC controller.