Please use this identifier to cite or link to this item:
Title: Machine-learning-based model predictive control with instantaneous linearization - a case study on an air-conditioning and mechanical ventilation system
Authors: Yang, Shiyu
Wan, Man Pun
Keywords: Engineering::Mechanical engineering
Issue Date: 2022
Source: Yang, S. & Wan, M. P. (2022). Machine-learning-based model predictive control with instantaneous linearization - a case study on an air-conditioning and mechanical ventilation system. Applied Energy, 306, 118041-.
Project: MOE/2020/MDT85_CIC 
Journal: Applied Energy 
Abstract: Machine-learning (ML) –based building models have been gaining popularity in constructing model predictive control (MPC) for building energy management applications. However, ML-based building models are usually nonlinear so to capture the building dynamics, leading to high computation load for MPC, prohibiting its application for real-time building control. This study proposes a ML-based MPC with an instantaneous linearization (IL) scheme, which employs real-time building operation data to linearize the nonlinear ML-based building model for constructing a linear MPC at each control interval. The proposed ML-based MPC with IL system is implemented to control an air conditioning system in an office of a general hospital building located in Singapore for experimental evaluation of its control performance. The ML-based MPC with IL is compared to a ML-based MPC that directly uses a nonlinear ML-based building model and the original reactive-control-based thermostat of the office. Results show that the ML-based MPC with IL significantly reduced the computation time (by more than 70 times) as compared to the ML-based MPC while retained most of the advantages of the ML-based MPC. The ML-based MPC with IL and the ML-based MPC achieved 31.6% and 26.0% reductions, respectively, in cooling energy consumption as compared to the original thermostat. Meanwhile, both the MPC systems significantly improved indoor thermal comfort for the office as compared to the original thermostat. The study demonstrated that using IL for ML-based MPC could substantially improve computation efficiency with no obvious performance degradation in terms of thermal comfort and energy saving.
ISSN: 0306-2619
DOI: 10.1016/j.apenergy.2021.118041
Schools: School of Mechanical and Aerospace Engineering 
Research Centres: Energy Research Institute @ NTU (ERI@N) 
Rights: © 2021 Published by Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:ERI@N Journal Articles
MAE Journal Articles

Citations 20

Updated on Oct 1, 2023

Web of ScienceTM
Citations 20

Updated on Oct 3, 2023

Page view(s)

Updated on Oct 3, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.