All you need to know about model predictive control for buildings

Drgoňa, Ján; Arroyo, Javier; Cupeiro Figueroa, Iago; Blum, David; Arendt, Krzysztof; Kim, Donghun; Ollé, Enric Perarnau; Oravec, Juraj; Wetter, Michael; Vrabie, Draguna L.; Helsen, Lieve

doi:10.1016/j.arcontrol.2020.09.001

Title: All you need to know about model predictive control for buildings

Journal Article · Tue Sep 01 00:00:00 EDT 2020 · Annual Reviews in Control

DOI:https://doi.org/10.1016/j.arcontrol.2020.09.001· OSTI ID:1668444

Drgoňa, Ján; Arroyo, Javier; Cupeiro Figueroa, Iago; Blum, David; Arendt, Krzysztof; Kim, Donghun; Ollé, Enric Perarnau; Oravec, Juraj; Wetter, Michael; Vrabie, Draguna L.; Helsen, Lieve

It has been proven that advanced building control, like model predictive control (MPC), can notably reduce the energy use and mitigate greenhouse gas emissions. Despite this fact and increased research activity in this field, the application of MPC in practice is still in its early stages. There are multiple reasons why. In particular, an MPC design is not trivial and requires an accurate control-oriented model of the building. Moreover, MPC implementation imposes increased requirements regarding communication infrastructure, computational power, and dedicated software tools. All these features make the implementation of MPC in real buildings an engineering challenge requiring a team of experts with a background in physics-based modeling, advanced control theory, optimization, and communication technologies. There is a growing need for multidisciplinary education and a unified framework for MPC in the built environment to be accessible for a broad range of researchers and practitioners with different engineering backgrounds. This paper provides a unified framework for model predictive building control technology with focus on the real-world application, reviewing the topic from both control and building engineering perspectives. From a theoretical point of view, this paper presents an overview of MPC formulations for building control, modeling paradigms and model types, together with algorithms necessary for real-life implementation. The paper categorizes the most notable MPC problem classes, links them with corresponding solution techniques, and provides an overview of methods for mitigation of the uncertainties for increased performance and robustness of MPC. From a practical point of view, this paper delivers an elaborate classification of the most important modeling, co-simulation, optimal control design, and optimization techniques, tools, and solvers suitable to tackle the MPC problems in the context of building climate control. On top of this, the paper presents the essential components of a practical implementation of MPC such as different control architectures and nuances of communication infrastructures within supervisory control and data acquisition (SCADA) systems. The paper draws practical guidelines with a generic workflow for implementation of MPC in real buildings aimed for contemporary adopters of this technology. Finally, the importance of standardized performance assessment and methodology for comparison of different building control algorithms is discussed.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office; European Research Council (ERC); Flemish Institute for Technological Research (VITO)

Grant/Contract Number:: AC05-76RL01830; AC02-05CH11231; 723649; 731231; 1710754

OSTI ID:: 1668444

Alternate ID(s):: OSTI ID: 1670130; OSTI ID: 1764024

Report Number(s):: PNNL-SA-153009; S1367578820300584; PII: S1367578820300584

Journal Information:: Annual Reviews in Control, Journal Name: Annual Reviews in Control; ISSN 1367-5788

Publisher:: International Federation of Automatic Control - ElsevierCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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