Volume 60

Iterative Machine-Learning-Based Model Predictive Control for Split Air Conditioners: A Cloud-Based Field Demonstration Li Wei, Dong Suning, Li Kun, Jia Qiwei, Liu Shiyong, Zheng Wei, Wan Man Pun, Xu Mingjun, Yang Shiyu

Abstract

In recent years, the Model Predictive Control (MPC) for building systems has received extensive attention, but few studies have applied machine learning (ML)-based MPC to split air conditioners. This paper presents a field implementation of an iterative ML-based MPC with a cloud-based framework to demonstrate its feasibility and energy-saving potential for split air conditioners. The measured results show that, compared with conventional PID control, MPC achieved an average energy-saving rate of 22.9%. In addition, iteratively updating the predictive model enables MPC to achieve enhanced thermal comfort. Hence, this paper demonstrates the energy-saving capability of ML-MPC for split air conditioners and potential scalable cloud-based deployment in residential buildings.

Keywords Split Air Conditioner, Model Predictive Control (MPC), Machine Learning (ML), Energy Efficiency