@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 {Berouine20229127,
	title = {A predictive control approach for thermal energy management in buildings},
	journal = {Energy Reports},
	volume = {8},
	year = {2022},
	note = {cited By 2},
	pages = {9127-9141},
	abstract = {Building equipment accounts for almost 40\% of total global energy consumption. More than half of which is used by active systems, such as heating, ventilation and air conditioning (HVAC) systems. These latter are responsible for the occupants{\textquoteright} well-being and considered among the main consumers of electricity in buildings. In order to improve both occupants{\textquoteright} comfort and energy efficiency in buildings, optimal control oriented models, such as Model Predictive Control (MPC), have proven to be promising techniques for developing intelligent control strategies for building energy management systems. This paper presents a real-time predictive control approach of an air conditioning (AC) system for thermal regulation in a single-zone building using MPC control framework. The proposed approach takes into account the physical parameters of the building, weather predictions (i.e. ambient temperature and solar radiation) and time-varying thermal comfort constraints to maintain optimal energy consumption of the AC while enhancing occupants{\textquoteright} comfort. For this purpose, a control-oriented thermal model for a room integrated with AC system is first developed using physics-based (white box) technique and then used to design and develop the MPC controller model. A numerical case study has been investigated and simulation results show the effectiveness of the proposed approach in reducing the energy consumption by about 68\% while providing a significant indoor thermal improvement. A conventional On{\textendash}Off controller was used as a baseline reference to evaluate the system performance against the proposed approach. {\textcopyright} 2022},
	keywords = {Building energy management systems, Building Thermal models, Buildings, Conditioning systems, Control approach, Controllers, energy efficiency, Energy management, Energy management systems, Energy utilization, Energy-consumption, Heating, Heating ventilation and air conditioning, HVAC, Model predictive control, Model-predictive control, Occupant comforts, Predictive control, Predictive control systems, Thermal energy, Thermography (temperature measurement), ventilation and air conditioning system},
	doi = {10.1016/j.egyr.2022.07.037},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134798712\&doi=10.1016\%2fj.egyr.2022.07.037\&partnerID=40\&md5=24dfba21dfceacb7c034ce19fec5ffec},
	author = {Berouine, A. and Ouladsine, R. and Bakhouya, M. and Essaaidi, M.}
}
@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.}
}
@article {Berouine2020,
	title = {Towards a real-time predictive management approach of indoor air quality in energy-efficient buildings},
	journal = {Energies},
	volume = {13},
	number = {12},
	year = {2020},
	note = {cited By 6},
	abstract = {Ventilation, heating and air conditioning systems are the main energy consumers in building sector. Improving the energy consumption of these systems, while satisfying the occupants{\textquoteright} comfort, is the major concern of control and automation designers and researchers. Model predictive control (MPC) methods have been widely studied in order to reduce the energy usage while enhancing the occupants{\textquoteright} comfort. In this paper, a generalized predictive control (GPC) algorithm based on controlled auto-regressive integrated moving average is investigated for standalone ventilation systems{\textquoteright} control. A building{\textquoteright}s ventilation system is first modeled together with the GPC and MPC controllers. Simulations have been conducted for validation purposes and are structured into two main parts. In the first part, we compare the MPC with two traditional controllers, while the second part is dedicated to the comparison of the MPC against the GPC controller. Simulation results show the effectiveness of the GPC in reducing the energy consumption by about 4.34\% while providing significant indoor air quality improvement. {\textcopyright} 2020 by the authors.},
	keywords = {Air conditioning, Air quality, Auto-regressive integrated moving average, Control and automation, Controllers, Energy consumer, energy efficiency, Energy efficient building, Energy utilization, Generalized predictive control, Heating-and-air conditioning system, Indoor air pollution, Indoor air quality, Intelligent buildings, Model predictive control, Predictive control systems, Ventilation, Ventilation systems},
	doi = {10.3390/en13123246},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089987718\&doi=10.3390\%2fen13123246\&partnerID=40\&md5=bdf8cea22143a02d8648fcb8c208a7b9},
	author = {Berouine, A. and Ouladsine, R. and Bakhouya, M. 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.}
}
