Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/160297
Title: Experiment study of machine-learning-based approximate model predictive control for energy-efficient building control
Authors: Yang, Shiyu
Wan, Man Pun
Chen, Wanyu
Ng, Bing Feng
Dubey, Swapnil
Keywords: Engineering::Mechanical engineering
Issue Date: 2021
Source: Yang, S., Wan, M. P., Chen, W., Ng, B. F. & Dubey, S. (2021). Experiment study of machine-learning-based approximate model predictive control for energy-efficient building control. Applied Energy, 288, 116648-. https://dx.doi.org/10.1016/j.apenergy.2021.116648
Project: N190107T00
2019-0607
NRF2016IDM-TRANS001-031
Journal: Applied Energy
Abstract: The adoption of model predictive control (MPC) for building automation and control applications is challenged by the high hardware and software requirements to solve its optimization problem. This study proposes an approximate MPC that mimics the dynamic behaviours of MPC using the recurrent neural network with a structure of nonlinear autoregressive network with exogenous inputs. The approximate MPC is developed by learning from the measured operation data of buildings controlled by MPC, therefore it can produce MPC-like control for buildings without needing to solve the optimization problem, significantly reducing the computation load as compared to MPC. The proposed approximate MPC is implemented in two testbeds, an office and a lecture theatre, to control the air-conditioning systems. The control performance of the approximate MPC is compared to MPC as well as the original reactive control of the two testbeds. The approximate MPC retained most of the energy and thermal comfort performance of MPC in both testbeds. For the office, the MPC and approximate MPC reduced 58.5% and 51.6% of cooling energy consumption, respectively, as compared to the original control. For the lecture theatre, the MPC and approximate MPC reduced 36.7% and 36.2% of cooling energy consumption, respectively, as compared to the original control. Meanwhile, both approximate MPC and MPC significantly improved indoor thermal comfort in the two testbeds as compared to their original control. Despite having minor degradation in control performance the approximate MPC was more than 100 times faster than MPC in generating optimal control commands in each time step.
URI: https://hdl.handle.net/10356/160297
ISSN: 0306-2619
DOI: 10.1016/j.apenergy.2021.116648
Schools: School of Mechanical and Aerospace Engineering 
Research Centres: Energy Research Institute @ NTU (ERI@N) 
Rights: © 2021 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:ERI@N Journal Articles
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