All you need to know about model predictive control for buildings

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

doi:10.1016/j.arcontrol.2020.09.001

Cited by 412 publications

(168 citation statements)

References 249 publications

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Section: Use Case 3: Optimal Control Of Hvacmentioning

confidence: 99%

“…Optimal control strategies, such as model-predictive control (MPC), have the potential to significantly reduce building energy consumption, demand, and cost and improve comfort compared to traditional rule-based control sequences [125]. MPC strategies compute optimal control inputs by minimizing an objective function, given a set of constraints, over a finite prediction horizon [125]. In the past two decades, MPC has received significant attention by the building research community, but it has not yet been implemented at scale, due, among other things, to the significant effort required to configure its models [126].…”

Section: Use Case 3: Optimal Control Of Hvacmentioning

confidence: 99%

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Metadata Schemas and Ontologies for Building Energy Applications: A Critical Review and Use Case Analysis

et al. 2021

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Digital and intelligent buildings are critical to realizing efficient building energy operations and a smart grid. With the increasing digitalization of processes throughout the life cycle of buildings, data exchanged between stakeholders and between building systems have grown significantly. However, a lack of semantic interoperability between data in different systems is still prevalent and hinders the development of energy-oriented applications that can be reused across buildings, limiting the scalability of innovative solutions. Addressing this challenge, our review paper systematically reviews metadata schemas and ontologies that are at the foundation of semantic interoperability necessary to move toward improved building energy operations. The review finds 40 schemas that span different phases of the building life cycle, most of which cover commercial building operations and, in particular, control and monitoring systems. The paper’s deeper review and analysis of five popular schemas identify several gaps in their ability to fully facilitate the work of a building modeler attempting to support three use cases: energy audits, automated fault detection and diagnosis, and optimal control. Our findings demonstrate that building modelers focused on energy use cases will find it difficult, labor intensive, and costly to create, sustain, and use semantic models with existing ontologies. This underscores the significant work still to be done to enable interoperable, usable, and maintainable building models. We make three recommendations for future work by the building modeling and energy communities: a centralized repository with a search engine for relevant schemas, the development of more use cases, and better harmonization and standardization of schemas in collaboration with industry to facilitate their adoption by stakeholders addressing varied energy-focused use cases.

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Section: Use Case 3: Optimal Control Of Hvacmentioning

confidence: 99%

Section: Use Case 3: Optimal Control Of Hvacmentioning

confidence: 99%

Metadata Schemas and Ontologies for Building Energy Applications: A Critical Review and Use Case Analysis

et al. 2021

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“…where u is the thermostat set-point, α determines the slope of the sigmoid function, and offset ( ) is an offset that models the physical distance between the temperature sensors in the room and the thermostats of the radiators. The building dynamics model are the following [1]:…”

Section: Building Modelmentioning

confidence: 99%

“…Such models enable the system to become smart using e.g. Model Predictive Control (MPC) [1]. MPC is a control method that minimises some predefined cost function while satisfying a set of constraints.…”

Section: Introductionmentioning

confidence: 99%

Non-linear Model Predictive Control for Smart Heating of Buildings

et al. 2021

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Smart and flexible operation of components in district heating systems can play a crucial role in integrating larger shares of renewable energy sources in energy systems. Buildings are one of the crucial components that will enable flexibility in the district heating by using intelligent operation. Recent work suggests that such improved operation at the same time can increase thermal comfort and lower economic costs. We have digitalised the heating system in a Danish school by adding IoT devices, such as smart thermostats and temperature sensors to demonstrate the possibilities of making buildings smart. Based on experimental data, this paper introduces a non-linear grey-box model of the thermal dynamics of the building. A non-linear model predictive control method is presented for the thermostatic set-point control of the building's radiators. Based on the building model and the control algorithm, simulation studies are carried out to show the flexibility potential of the building. When used for lowering the return temperature the results suggest that operational costs can be lowered by around 10% using predictive control.

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“…To overcome such limitations, the application of model-based control strategies has been explored in the last few years. In particular, Model Predicted Control (MPC), has become a dominant control strategy in research on intelligent building operation [12]. However, despite the implementation of MPC has shown its excellent ability of improving thermal comfort and reducing energy consumption between 15 and 50% [13][14][15], in both simulative [16] and real test environments [17], its model-based nature represents in some cases a critical aspect.…”

Section: Introductionmentioning

confidence: 99%

Online Implementation of a Soft Actor-Critic Agent to Enhance Indoor Temperature Control and Energy Efficiency in Buildings

et al. 2021

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Recently, a growing interest has been observed in HVAC control systems based on Artificial Intelligence, to improve comfort conditions while avoiding unnecessary energy consumption. In this work, a model-free algorithm belonging to the Deep Reinforcement Learning (DRL) class, Soft Actor-Critic, was implemented to control the supply water temperature to radiant terminal units of a heating system serving an office building. The controller was trained online, and a preliminary sensitivity analysis on hyperparameters was performed to assess their influence on the agent performance. The DRL agent with the best performance was compared to a rule-based controller assumed as a baseline during a three-month heating season. The DRL controller outperformed the baseline after two weeks of deployment, with an overall performance improvement related to control of indoor temperature conditions. Moreover, the adaptability of the DRL agent was tested for various control scenarios, simulating changes of external weather conditions, indoor temperature setpoint, building envelope features and occupancy patterns. The agent dynamically deployed, despite a slight increase in energy consumption, led to an improvement of indoor temperature control, reducing the cumulative sum of temperature violations on average for all scenarios by 75% and 48% compared to the baseline and statically deployed agent respectively.

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All you need to know about model predictive control for buildings

Cited by 412 publications

References 249 publications

Metadata Schemas and Ontologies for Building Energy Applications: A Critical Review and Use Case Analysis

Metadata Schemas and Ontologies for Building Energy Applications: A Critical Review and Use Case Analysis

Non-linear Model Predictive Control for Smart Heating of Buildings

Online Implementation of a Soft Actor-Critic Agent to Enhance Indoor Temperature Control and Energy Efficiency in Buildings

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