@article {Kharbouch2022,
	title = {Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation},
	journal = {Sensors},
	volume = {22},
	number = {20},
	year = {2022},
	note = {cited By 0},
	abstract = {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 (CO2) 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{\textquoteright} 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. {\textcopyright} 2022 by the authors.},
	keywords = {Air conditioning, Air pollution, Building ventilations, Carbon dioxide, Control approach, Control program, Data platform, energy efficiency, Energy utilization, Hardware in the loops, Hardware-in-the-loop simulation, Hardwarein-the-loop simulations (HIL), Indoor, Indoor air pollution, Internet of things, Learning algorithms, Long short-term memory, Machine learning algorithms, Machine-learning, MATLAB, Model predictive control, Model-predictive control, Predictive control, procedures, Simulation platform, Synthetic apertures, Ventilation},
	doi = {10.3390/s22207978},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140844555\&doi=10.3390\%2fs22207978\&partnerID=40\&md5=85b1a27cdc872a2b2f27b188c17282c8},
	author = {Kharbouch, A. and Berouine, A. and Elkhoukhi, H. and Berrabah, S. and Bakhouya, M. and El Ouadghiri, D. and Gaber, J.}
}
@article {Elkhoukhi2020,
	title = {A platform architecture for occupancy detection using stream processing and machine learning approaches},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {32},
	number = {17},
	year = {2020},
	note = {cited By 15},
	abstract = {Context-awareness in energy-efficient buildings has been considered as a crucial fact for developing context-driven control approaches in which sensing and actuation tasks are performed according to the contextual changes. This could be done by including the presence of occupants, number, actions, and behaviors in up-to-date context, taking into account the complex interlinked elements, situations, processes, and their dynamics. However, many studies have shown that occupancy information is a major leading source of uncertainty when developing control approaches. Comprehensive and real-time fine-grained occupancy information has to be, therefore, integrated in order to improve the performance of occupancy-driven control approaches. The work presented in this paper is toward the development of a holistic platform that combines recent IoT and Big Data technologies for real-time occupancy detection in smart building. The purpose of this work focuses mainly on the presence of occupants by comparing both static and dynamic machine learning techniques. An open-access occupancy detection dataset was first used to assess the usefulness of the platform and the effectiveness of static machine learning strategies for data processing. This dataset is used for applications that follow the strategy aiming at storing data first and processing it later. However, many smart buildings{\textquoteright} applications, such as HVAC and ventilation control, require online data streams processing. Therefore, a distributed real-time machine learning framework was integrated into the platform and tested to show its effectiveness for this kind of applications. Experiments have been conducted for ventilation systems in energy-efficient building laboratory (EEBLab) and preliminary results show the effectiveness of this platform in detecting on-the-fly presence of occupants, which is required to either make ON or OFF the system and then activate the corresponding embedded control technique (eg, ON/OFF, PID, state-feedback). {\textcopyright} 2019 John Wiley \& Sons, Ltd.},
	keywords = {Context- awareness, Data streams, Embedded systems, energy efficiency, Energy efficient building, Intelligent buildings, Internet of things, Learning systems, Machine learning, Machine learning approaches, Machine learning techniques, Occupancy detections, Platform architecture, Real-time data processing, State feedback, Ventilation, Ventilation control},
	doi = {10.1002/cpe.5651},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076920865\&doi=10.1002\%2fcpe.5651\&partnerID=40\&md5=f34bbe0cd8dadd04282227e1a5698c57},
	author = {Elkhoukhi, H. and NaitMalek, Y. and Bakhouya, M. and Berouine, A. and Kharbouch, A. and Lachhab, F. and Hanifi, M. and El Ouadghiri, D. and Essaaidi, M.}
}
@conference {Elkhoukhi2018114,
	title = {Towards a Real-time Occupancy Detection Approach for Smart Buildings},
	booktitle = {Procedia Computer Science},
	volume = {134},
	year = {2018},
	pages = {114-120},
	doi = {10.1016/j.procs.2018.07.151},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051384064\&doi=10.1016\%2fj.procs.2018.07.151\&partnerID=40\&md5=952a828342b9a8bac2792cc053d73477},
	author = {Elkhoukhi, H. and NaitMalek, Y. and Berouine, A. and Bakhouya, M. and Elouadghiri, D. and Essaaidi, M.}
}
