Experimental data-driven model predictive control of a hospital HVAC system during regular use

被引:15
作者
Maddalena, Emilio T. [1 ]
Mueller, Silvio A. [1 ]
dos Santos, Rafael M. [2 ]
Salzmann, Christophe [1 ]
Jones, Colin N. [1 ]
机构
[1] Ecole Polytech Fed Lausanne EPFL, Lab Automat, Lausanne, Switzerland
[2] Hosp Sao Juliao, Campo Grande, MS, Brazil
基金
瑞士国家科学基金会;
关键词
HVAC systems; Model predictive control; Gaussian processes; Data -driven methods; BAYESIAN CALIBRATION; ENERGY MODELS; OPTIMIZATION;
D O I
10.1016/j.enbuild.2022.112316
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Herein we report a multi-zone, heating, ventilation and air-conditioning (HVAC) control case study of an industrial plant responsible for cooling a hospital surgery center. The adopted approach to guaranteeing thermal comfort and reducing electrical energy consumption is based on a statistical non-parametric, non-linear regression technique named Gaussian processes. Our study aimed at assessing the suitability of the aforementioned technique to learning the building dynamics and yielding models for our model predictive control (MPC) scheme. Experimental results gathered while the building was under regular use showcase the final controller performance while subject to a number of measured and unmeasured disturbances. Finally, we provide readers with practical details and recommendations on how to manage the computational complexity of the on-line optimization problem and obtain high-quality solutions from solvers. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
引用
收藏
页数:10
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