Model predictive control for thermal comfort optimization in building energy management systems

Carli, Raffaele; Cavone, Graziana; Dotoli, Mariagrazia; Scarabaggio, Paolo

doi:10.1109/smc.2019.8914489

Cited by 18 publications

(10 citation statements)

References 18 publications

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Section: Related Work and Paper Contributionsmentioning

confidence: 94%

“…We underline that this work extends our baseline article [25], where we present a preliminary version of our MPC algorithm aiming at optimizing the thermal comfort and energy efficiency of indoor environments. In [25] we consider the optimization of a non-linear thermal comfort index together with a fixed cost energy consumption function, subject to strict constraints on comfort requirements. This results in a non-linear optimization problem that on the one hand allows the precise representation of the thermal comfort, but on the other hand prevents the applicability of the technique in case of partial knowledge of the thermal parameters of the considered environment and in case of physical limits of the actuation system.…”

Section: Related Work and Paper Contributionsmentioning

confidence: 94%

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IoT Based Architecture for Model Predictive Control of HVAC Systems in Smart Buildings

Carli

Cavone

Othman

et al. 2020

Sensors

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100

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The efficient management of Heating Ventilation and Air Conditioning (HVAC) systems in smart buildings is one of the main applications of the Internet of Things (IoT) paradigm. In this paper we propose an IoT based architecture for the implementation of Model Predictive Control (MPC) of HVAC systems in real environments. The considered MPC algorithm optimizes on line, in a closed-loop control fashion, both the indoor thermal comfort and the related energy consumption for a single zone environment. Thanks to the proposed IoT based architecture, the sensing, control, and actuating subsystems are all connected to the Internet, and a remote interface with the HVAC control system is guaranteed to end-users. In particular, sensors and actuators communicate with a remote database server and a control unit, which provides the control actions to be actuated in the HVAC system; users can set remotely the control mode and related set-points of the system; while comfort and environmental indices are transferred via the Internet and displayed on the end-users' interface. The proposed IoT based control architecture is implemented and tested in a campus building at the Polytechnic of Bari (Italy) in a proof of concept perspective. The effectiveness of the proposed control algorithm is assessed in the real environment evaluating both the thermal comfort results and the energy savings with respect to a classical thermostat regulation approach.

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Section: Related Work and Paper Contributionsmentioning

confidence: 94%

Section: Related Work and Paper Contributionsmentioning

confidence: 94%

IoT Based Architecture for Model Predictive Control of HVAC Systems in Smart Buildings

Carli

Cavone

Othman

et al. 2020

Sensors

Self Cite

100

View full text Add to dashboard Cite

show abstract

“…e main idea in Ref. [24][25][26] is to improve the efficiency of building energy management systems (BEMS). e authors in Ref.…”

Section: Related Workmentioning

confidence: 99%

Skeleton of Implementing Voice Control for Building Automation Systems

Dweik

Abdalla

AlHroob

et al. 2022

Scientific Programming

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Building automation is one of the most popular trends in the 21st century, as it provides buildings with modern and luxurious touches. Furthermore, voice-controlled automation systems are very convenient for the current fast-paced life style as people need to quickly, efficiently, and safely control everything around them. The goal of this article is to provide a skeleton for implementing voice control to a building automation system. The article also presents and evaluates a use case of the proposed skeleton integrated with a KNX-based office automation system. Through the integrated system, lights and blinds are controlled and sensors are polled using voice commands from an authorized personal. This article serves as an educational reference for engineering students interested in applying authorized voice control capability to automation systems.

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“…In this perspective, Model Predictive Control (MPC) is a control technique including both feedback control and optimization that allows to take into account the deviations of the predictive model from the real progress of the disease Bussell, Dangerfield, Gilligan, and Cunniffe (2019) , Morato, Normey-Rico, and Sename (2020) . Although implementing the MPC controller typically requires a large amount of computational resources, which can lead to long computation time Carli, Cavone, Dotoli, Epicoco, and Scarabaggio (2019) , this is not a concern when the optimization is performed at a strategic level, as is the case of the decision-making process for the definition of the proper strategies to tackle epidemiological diseases. The basic idea here is to keep the true system state (that is, the predicted future output of the model) in line with the selected target.…”

Section: Introduction and Paper Positioningmentioning

confidence: 99%

Model predictive control to mitigate the COVID-19 outbreak in a multi-region scenario

Carli

Cavone

Epicoco

et al. 2020

Annual Reviews in Control

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The COVID-19 outbreak is deeply influencing the global social and economic framework, due to restrictive measures adopted worldwide by governments to counteract the pandemic contagion. In multi-region areas such as Italy, where the contagion peak has been reached, it is crucial to find targeted and coordinated optimal exit and restarting strategies on a regional basis to effectively cope with possible onset of further epidemic waves, while efficiently returning the economic activities to their standard level of intensity. Differently from the related literature, where modeling and controlling the pandemic contagion is typically addressed on a national basis, this paper proposes an optimal control approach that supports governments in defining the most effective strategies to be adopted during post-lockdown mitigation phases in a multi-region scenario. Based on the joint use of a non-linear Model Predictive Control scheme and a modified Susceptible-Infected-Recovered (SIR)-based epidemiological model, the approach is aimed at minimizing the cost of the so-called non-pharmaceutical interventions (that is, mitigation strategies), while ensuring that the capacity of the network of regional healthcare systems is not violated. In addition, the proposed approach supports policy makers in taking targeted intervention decisions on different regions by an integrated and structured model, thus both respecting the specific regional health systems characteristics and improving the system-wide performance by avoiding uncoordinated actions of the regions. The methodology is tested on the COVID-19 outbreak data related to the network of Italian regions, showing its effectiveness in properly supporting the definition of effective regional strategies for managing the COVID-19 diffusion.

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Model predictive control for thermal comfort optimization in building energy management systems

Cited by 18 publications

References 18 publications

IoT Based Architecture for Model Predictive Control of HVAC Systems in Smart Buildings

IoT Based Architecture for Model Predictive Control of HVAC Systems in Smart Buildings

Skeleton of Implementing Voice Control for Building Automation Systems

Model predictive control to mitigate the COVID-19 outbreak in a multi-region scenario

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