Comfort-Aware Building Climate Control Using Distributed-Parameter Models

TL;DR

This paper presents a comfort-aware HVAC control system using a PDE-based model predictive controller that optimizes energy efficiency while maintaining indoor thermal comfort, demonstrated through simulations of a real apartment.

Contribution

It introduces a PDE-constrained MPC approach with a CFD model for precise spatial control of indoor climate based on only thermostat data.

Findings

Enhanced energy efficiency in HVAC control.

Accurate spatial temperature distribution modeling.

Potential for real-time high-performance building management.

Abstract

Controlling Heating, Ventilation and Air Conditioning (HVAC) system to maintain occupant's indoor thermal comfort is important to energy-efficient buildings and the development of smart cities. In this paper, we formulate a model predictive controller (MPC) to make optimal control strategies to HVAC in order to maintain occupant's comfort by predicted mean vote index, and then present a whole system with an estimator to indoor climate and apartment's geometric information based on only thermostats. In order to have accurate spatial resolution and make the HVAC system focus on only a zoned area around the occupant, a convection-diffusion Computer Fluid Dynamics (CFD) model is used to describe the indoor air flow and temperature distribution. The MPC system generates corresponding PDE-constrained optimization problems, and we solve them by obtain the gradients of cost functions with respect to problems' variables with the help of CFD model's adjoint equations. We evaluate the performance of our method using simulations of a real apartment in the St.\ Louis area. Our results show our MPC system's energy efficiency and the potential for its application in real-time operation of high-performance buildings.