Ventilation and Temperature Control for Energy-efficient and Healthy Buildings: A Differentiable PDE Approach

TL;DR

This paper presents a differentiable PDE-based framework for optimizing ventilation and temperature control in buildings, achieving energy efficiency and healthy indoor environments through system learning and control optimization.

Contribution

It introduces a novel PDE-based approach for building control that outperforms traditional methods in energy savings and safety constraint adherence.

Findings

Significant reduction in energy consumption compared to traditional methods.

Effective joint learning of temperature and CO2 fields from data.

Maintains indoor air quality and comfort while optimizing energy use.

Abstract

In this paper, we introduce a novel framework for building learning and control, focusing on ventilation and thermal management to enhance energy efficiency. We validate the performance of the proposed framework in system model learning via two case studies: a synthetic study focusing on the joint learning of temperature and CO2 fields, and an application to a real-world dataset for CO2 field learning. For building control, we demonstrate that the proposed framework can optimize the control actions and significantly reduce the energy cost while maintaining a comfort and healthy indoor environment. When compared to existing traditional methods, an optimization-based method with ODE models and reinforcement learning, our approach can significantly reduce the energy consumption while guarantees all the safety-critical air quality and control constraints. Promising future research directions involve validating and improving the proposed PDE models through accurate estimation of airflow fields within indoor environments. Additionally, incorporating uncertainty modeling into the PDE framework for HVAC control presents an opportunity to enhance the efficiency and reliability of building HVAC system management.