Volume 60

An Adaptive Transfer-Learning-Assisted Modular Learning Approach for Control-Oriented Building Modelling Zheng Wei, Dong Suning, Wan Man Pun, Yang Shiyu

Abstract

Machine learning (ML)-based Model Predictive Control (MPC) has shown promise for smart buildings, with online ML model adaptation often integrated to enhance long-term performance. However, abrupt building thermodynamic changes, such as opening/closing windows, can hardly been captured by the conventional online ML model adaptation. An Adaptive Transfer-Learning-Assisted Modular Learning (ATML) framework combined with dataset truncation is proposed by this paper to address the challenge. A simulation case study using a residential building was conducted for evaluation. Results show that, compared with conventional online ML model adaptation, ATML reduces prediction errors by 32.4% after 2 hours of the systemic change and by 25.4% after 22 hours, thereby demonstrating its effectiveness in coping with abrupt systemic changes.

Keywords Model Predictive Control (MPC), Machine Learning (ML), Transfer Learning (TL)