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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| Format: | Article |
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MDPI AG
2022-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/20/7978 |
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| author | Abdelhak Kharbouch Anass Berouine Hamza Elkhoukhi Soukayna Berrabah Mohamed Bakhouya Driss El Ouadghiri Jaafar Gaber |
| author_facet | Abdelhak Kharbouch Anass Berouine Hamza Elkhoukhi Soukayna Berrabah Mohamed Bakhouya Driss El Ouadghiri Jaafar Gaber |
| author_sort | Abdelhak Kharbouch |
| collection | DOAJ |
| 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<sub>2</sub>) 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. |
| first_indexed | 2024-03-09T19:30:57Z |
| format | Article |
| id | doaj.art-aa29e8fda3d64db590cc5962da100e02 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T19:30:57Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-aa29e8fda3d64db590cc5962da100e022023-11-24T02:29:47ZengMDPI AGSensors1424-82202022-10-012220797810.3390/s22207978Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building VentilationAbdelhak Kharbouch0Anass Berouine1Hamza Elkhoukhi2Soukayna Berrabah3Mohamed Bakhouya4Driss El Ouadghiri5Jaafar Gaber6LERMA Lab, College of Engineering, The International University of Rabat, Technopolis Rabat-Shore Rocade Rabat-Salé, Sala El Jadida 11100, MoroccoLERMA Lab, College of Engineering, The International University of Rabat, Technopolis Rabat-Shore Rocade Rabat-Salé, Sala El Jadida 11100, MoroccoLERMA Lab, College of Engineering, The International University of Rabat, Technopolis Rabat-Shore Rocade Rabat-Salé, Sala El Jadida 11100, MoroccoLERMA Lab, College of Engineering, The International University of Rabat, Technopolis Rabat-Shore Rocade Rabat-Salé, Sala El Jadida 11100, MoroccoLERMA Lab, College of Engineering, The International University of Rabat, Technopolis Rabat-Shore Rocade Rabat-Salé, Sala El Jadida 11100, MoroccoI&A Laboratory, Faculty of Science, Moulay Ismail University of Meknès, B.P. 11201 Zitoune, Meknès 50070, MoroccoUniv. Bourgogne Franche-Comte, UTBM, FEMTO-ST UMR CNRS 6174, 25000 Belfort, FranceIn 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<sub>2</sub>) 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.https://www.mdpi.com/1424-8220/22/20/7978Internet of Thingsmodel predictive controlhardware in the loopmachine learningenergy efficiencysmart buildings |
| spellingShingle | Abdelhak Kharbouch Anass Berouine Hamza Elkhoukhi Soukayna Berrabah Mohamed Bakhouya Driss El Ouadghiri Jaafar Gaber Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation Sensors Internet of Things model predictive control hardware in the loop machine learning energy efficiency smart buildings |
| 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 | Internet of Things model predictive control hardware in the loop machine learning energy efficiency smart buildings |
| url | https://www.mdpi.com/1424-8220/22/20/7978 |
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