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

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          Abstract

          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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          Most cited references27

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          Model predictive control: Theory and practice—A survey

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            Theory and applications of HVAC control systems – A review of model predictive control (MPC)

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              A review on optimized control systems for building energy and comfort management of smart sustainable buildings

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                31 January 2020
                February 2020
                : 20
                : 3
                : 781
                Affiliations
                [1 ]Department of Electrical and Information Engineering, Polytechnic of Bari, Via Orabona 4, 70125 Bari, Italy; graziana.cavone@ 123456poliba.it (G.C.); mariagrazia.dotoli@ 123456poliba.it (M.D.)
                [2 ]CRIStAL Laboratory UML 9189, Ecole-Central of Lille, 59655 Villeneuve d’Ascq, France; sara.ben-othman@ 123456centralelille.fr
                Author notes
                [* ]Correspondence: raffaele.carli@ 123456poliba.it ; Tel.: +39-080-596-3843
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0001-9184-6017
                https://orcid.org/0000-0001-7102-4542
                Article
                sensors-20-00781
                10.3390/s20030781
                7038446
                32023965
                72b1e53c-1cc5-47a6-b0eb-a74f7b7301fa
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 16 December 2019
                : 28 January 2020
                Categories
                Article

                Biomedical engineering
                internet of things,model predictive control,heating ventilation and air conditioning system,predicted mean vote,smart buildings

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