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Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation

In this work, a Hardware-In-the-Loop (HIL) framework is introduced for the implementation and the assessment of predictive control approaches in smart buildings. The framework combines recent Internet of Things (IoT) and big data platforms together with machine-learning algorithms and MATLAB-based M...

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Autores principales: Kharbouch, Abdelhak, Berouine, Anass, Elkhoukhi, Hamza, Berrabah, Soukayna, Bakhouya, Mohamed, El Ouadghiri, Driss, Gaber, Jaafar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9609043/
https://www.ncbi.nlm.nih.gov/pubmed/36298333
http://dx.doi.org/10.3390/s22207978
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author Kharbouch, Abdelhak
Berouine, Anass
Elkhoukhi, Hamza
Berrabah, Soukayna
Bakhouya, Mohamed
El Ouadghiri, Driss
Gaber, Jaafar
author_facet Kharbouch, Abdelhak
Berouine, Anass
Elkhoukhi, Hamza
Berrabah, Soukayna
Bakhouya, Mohamed
El Ouadghiri, Driss
Gaber, Jaafar
author_sort Kharbouch, Abdelhak
collection PubMed
description In this work, a Hardware-In-the-Loop (HIL) framework is introduced for the implementation and the assessment of predictive control approaches in smart buildings. The framework combines recent Internet of Things (IoT) and big data platforms together with machine-learning algorithms and MATLAB-based Model Predictive Control (MPC) programs in order to enable HIL simulations. As a case study, the MPC algorithm was deployed for control of a standalone ventilation system (VS). The objective is to maintain the indoor Carbon Dioxide (CO(2)) concentration at the standard comfort range while enhancing energy efficiency in the building. The proposed framework has been tested and deployed in a real-case scenario of the EEBLab test site. The MPC controller has been implemented on MATLAB/Simulink and deployed in a Raspberry Pi (RPi) hardware. Contextual data are collected using the deployed IoT/big data platform and injected into the MPC and LSTM machine learning models. Occupants’ numbers were first forecasted and then sent to the MPC to predict the optimal ventilation flow rates. The performance of the MPC control over the HIL framework has been assessed and compared to an ON/OFF strategy. Results show the usefulness of the proposed approach and its effectiveness in reducing energy consumption by approximately 16%, while maintaining good indoor air quality.
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spelling pubmed-96090432022-10-28 Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation Kharbouch, Abdelhak Berouine, Anass Elkhoukhi, Hamza Berrabah, Soukayna Bakhouya, Mohamed El Ouadghiri, Driss Gaber, Jaafar Sensors (Basel) Article In this work, a Hardware-In-the-Loop (HIL) framework is introduced for the implementation and the assessment of predictive control approaches in smart buildings. The framework combines recent Internet of Things (IoT) and big data platforms together with machine-learning algorithms and MATLAB-based Model Predictive Control (MPC) programs in order to enable HIL simulations. As a case study, the MPC algorithm was deployed for control of a standalone ventilation system (VS). The objective is to maintain the indoor Carbon Dioxide (CO(2)) concentration at the standard comfort range while enhancing energy efficiency in the building. The proposed framework has been tested and deployed in a real-case scenario of the EEBLab test site. The MPC controller has been implemented on MATLAB/Simulink and deployed in a Raspberry Pi (RPi) hardware. Contextual data are collected using the deployed IoT/big data platform and injected into the MPC and LSTM machine learning models. Occupants’ numbers were first forecasted and then sent to the MPC to predict the optimal ventilation flow rates. The performance of the MPC control over the HIL framework has been assessed and compared to an ON/OFF strategy. Results show the usefulness of the proposed approach and its effectiveness in reducing energy consumption by approximately 16%, while maintaining good indoor air quality. MDPI 2022-10-19 /pmc/articles/PMC9609043/ /pubmed/36298333 http://dx.doi.org/10.3390/s22207978 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kharbouch, Abdelhak
Berouine, Anass
Elkhoukhi, Hamza
Berrabah, Soukayna
Bakhouya, Mohamed
El Ouadghiri, Driss
Gaber, Jaafar
Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation
title Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation
title_full Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation
title_fullStr Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation
title_full_unstemmed Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation
title_short Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation
title_sort internet-of-things based hardware-in-the-loop framework for model-predictive-control of smart building ventilation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9609043/
https://www.ncbi.nlm.nih.gov/pubmed/36298333
http://dx.doi.org/10.3390/s22207978
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