A thermal comfort-driven model predictive controller for residential split air conditioner

Abstract

In the wake of rising cooling electricity demand in the residential building sector, there is a need to contemplate how the residential air conditioners (ACs) are operated. This study explores the residential split AC's capacity regarding its ability to provide thermal comfort to the occupants instead of just cooling through the implementation of thermal comfort-driven model predictive control (MPC). The proposed control technique is illustrated by implementing it in a real residential house. To integrate thermal comfort feedback into the controller while optimizing AC's set-point, a real-time co-simulation methodology has been developed, using EnergyPlus and Simulink. A data-driven model is used in the MPC framework to predict the set-point of AC, accounting for the environmental variables as measured disturbances and thermal comfort index, i.e., predictive mean vote (PMV) as a constraint on output. A Raspberry Pi-Arduino-based embedded system is developed to actuate the AC's set-point during real-time co-simulation. Experimental data is used to develop the numerical model of the system. The simulation study has been performed to observe the proposed control technique's value addition over the base cases. Simulation data shows that AC operated with the proposed control technique consumes (8.8–17.5%) less cooling energy compared to the base cases.