@article {NaitMalek2022,
	title = {A Hybrid Approach for State-of-Charge Forecasting in Battery-Powered Electric Vehicles},
	journal = {Sustainability (Switzerland)},
	volume = {14},
	number = {16},
	year = {2022},
	note = {cited By 0},
	abstract = {Nowadays, electric vehicles (EV) are increasingly penetrating the transportation roads in most countries worldwide. Many efforts are oriented toward the deployment of the EVs infrastructures, including those dedicated to intelligent transportation and electro-mobility as well. For instance, many Moroccan organizations are collaborating to deploy charging stations in mostly all Moroccan cities. Furthermore, in Morocco, EVs are tax-free, and their users can charge for free their vehicles in any station. However, customers are still worried by the driving range of EVs. For instance, a new driving style is needed to increase the driving range of their EV, which is not easy in most cases. Therefore, the need for a companion system that helps in adopting a suitable driving style arise. The driving range depends mainly on the battery{\textquoteright}s capacity. Hence, knowing in advance the battery{\textquoteright}s state-of-charge (SoC) could help in computing the remaining driving range. In this paper, a battery SoC forecasting method is introduced and tested in a real case scenario on Rabat-Sal{\'e}-K{\'e}nitra urban roads using a Twizy EV. Results show that this method is able to forecast the SoC up to 180 s ahead with minimal errors and low computational overhead, making it more suitable for deployment in in-vehicle embedded systems. {\textcopyright} 2022 by the authors.},
	keywords = {electric vehicle, forecasting method, Morocco, transportation},
	doi = {10.3390/su14169993},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137735443\&doi=10.3390\%2fsu14169993\&partnerID=40\&md5=95a2dbf1fda5d9cc89e011adc1137835},
	author = {NaitMalek, Y. and Najib, M. and Lahlou, A. and Bakhouya, M. and Gaber, J. and Essaaidi, M.}
}
@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 {NaitMalek2021,
	title = {Embedded Real-time Battery State-of-Charge Forecasting in Micro-Grid Systems},
	journal = {Ecological Complexity},
	volume = {45},
	year = {2021},
	note = {cited By 2},
	abstract = {Micro-grid systems (MGS) are increasingly investigated for green and energy efficient buildings in order to reduce energy consumption while maintaining occupants{\textquoteright} comfort. It includes renewable energy sources for power production, storage devices for storing power excess, and control strategies for orchestrating all components and improving the system{\textquoteright}s efficiency. In fact, MGS can be seen as complex systems composed of different heterogeneous entities that interact dynamically and in collective manner in order to balance between energy efficiency and occupants{\textquoteright} comfort. However, the uncertainty and intermittency of energy production and consumption requires the development of real-time forecasting methods and predictive control strategies. The State-of-Charge (SoC) of batteries is one of the main parameters used in MGS predictive control algorithms. It indicates how much energy is stored and how long MGS can be relying on deployed storage devices. Several methods have been developed for SoC estimation, but little work, however, has been dedicated for SoC forecasting in MGS. In this paper, we focus on advancing MGS predictive control through near real-time embedded forecasting of batteries SoC. In fact, we have deployed, on two platforms, two forecasting methods, Long Short-Term Memory (LSTM) and Auto Regressive Integrated Moving Average (ARIMA). Their accuracy and performance have been evaluated in both classical batch mode and streaming mode. Extensive experiments have been conducted for different forecasting horizons and results are presented using two main metrics, the accuracy and the computational time. Obtained results show that LSTM outperforms ARIMA for real-time forecasting, it has the better tradeoff in terms of forecasting accuracy and performance. {\textcopyright} 2020 Elsevier B.V.},
	doi = {10.1016/j.ecocom.2020.100903},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098467418\&doi=10.1016\%2fj.ecocom.2020.100903\&partnerID=40\&md5=e995cf66a3ed47b77bb78e95b8628d9d},
	author = {NaitMalek, Y. and Najib, M. and Bakhouya, M. and Essaaidi, M.}
}
@article {Elmouatamid2021571,
	title = {A MicroGrid System Infrastructure Implementing IoT/Big-Data Technologies for Efficient Energy Management in Buildings},
	journal = {Green Energy and Technology},
	year = {2021},
	note = {cited By 4},
	pages = {571-600},
	abstract = {Recent studies showed that energy consumption in buildings could be efficiently reduced by including recent IoT (Internet of Things) and Big-Data technologies into microgrid systems. In fact, three major aspects could be further considered for reducing energy consumption while maintaining a suitable occupants{\textquoteright} comfort, (i) integrating renewable energy sources and storage devices, (ii) integrating programmable and less-energy-consuming equipment, and (iii) deploying innovative information and communication technologies. These aspects might contribute substantially to the improvement of winning and saving energy toward smart and energy-efficient buildings. In this chapter, a microgrid system infrastructure is developed together with a platform for data gathering, monitoring, and processing. We put more emphasis on microgrid systems as crucial infrastructures for leveraging energy-efficient and smart buildings by developing and deploying a holistic IoT/Big-Data platform in which sensing and actuation tasks are performed according to the actual contextual changes. Scenarios are presented in order to show the usefulness of this holistic platform for monitoring, data processing, and control in energy-efficient buildings. {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	keywords = {Data gathering, Data handling, Data technologies, energy efficiency, Energy efficient, Energy efficient building, Energy utilization, Information and Communication Technologies, Intelligent buildings, Internet of things, Micro-grid systems, Microgrids, Monitoring, Reducing energy consumption, Renewable energy resources, Renewable energy source, Virtual storage},
	doi = {10.1007/978-3-030-64565-6_20},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105764631\&doi=10.1007\%2f978-3-030-64565-6_20\&partnerID=40\&md5=b0a762b2a1e37fc93eff19c16f955daa},
	author = {Elmouatamid, A. and NaitMalek, Y. and Ouladsine, R. and Bakhouya, M. and El kamoun, N. and Khaidar, M. and Zine-Dine, K.}
}
@article {Malek202156,
	title = {Multivariate deep learning approach for electric vehicle speed forecasting},
	journal = {Big Data Mining and Analytics},
	volume = {4},
	number = {1},
	year = {2021},
	note = {cited By 46},
	pages = {56-64},
	abstract = {Speed forecasting has numerous applications in intelligent transport systems{\textquoteright} design and control, especially for safety and road efficiency applications. In the field of electromobility, it represents the most dynamic parameter for efficient online in-vehicle energy management. However, vehicles{\textquoteright} speed forecasting is a challenging task, because its estimation is closely related to various features, which can be classified into two categories, endogenous and exogenous features. Endogenous features represent electric vehicles{\textquoteright} characteristics, whereas exogenous ones represent its surrounding context, such as traffic, weather, and road conditions. In this paper, a speed forecasting method based on the Long Short-Term Memory (LSTM) is introduced. The LSTM model training is performed upon a dataset collected from a traffic simulator based on real-world data representing urban itineraries. The proposed models are generated for univariate and multivariate scenarios and are assessed in terms of accuracy for speed forecasting. Simulation results show that the multivariate model outperforms the univariate model for short- and long-term forecasting. {\textcopyright} 2018 Tsinghua University Press.},
	keywords = {Deep learning, Design and control, Dynamic parameters, Electric vehicles, energy efficiency, Forecasting, Forecasting methods, Intelligent systems, Intelligent transport systems, Learning approach, Long short-term memory, Long-term forecasting, Multivariate modeling, Roads and streets, Speed, Traffic control, Traffic simulators, Vehicle actuated signals},
	doi = {10.26599/BDMA.2020.9020027},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099566517\&doi=10.26599\%2fBDMA.2020.9020027\&partnerID=40\&md5=2faaf6f427bd230fa39a873be4e45566},
	author = {Malek, Y.N. and Najib, M. and Bakhouya, M. and Essaaidi, M.}
}
@article {Bakhouya2020,
	title = {Cloud computing, IoT, and big data: Technologies and applications},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {32},
	number = {17},
	year = {2020},
	note = {cited By 1},
	doi = {10.1002/cpe.5896},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089609248\&doi=10.1002\%2fcpe.5896\&partnerID=40\&md5=7054f06999f02eff0eb333bc9115380d},
	author = {Bakhouya, M. and Zbakh, M. and Essaaidi, M. and Manneback, P.}
}
@article {Boulmrharj2020,
	title = {Online battery state-of-charge estimation methods in micro-grid systems},
	journal = {Journal of Energy Storage},
	volume = {30},
	year = {2020},
	note = {cited By 30},
	abstract = {Batteries have shown great potential for being integrated in Micro-Grid (MG) systems. However, their integration is not a trivial task and, therefore, requires extensive modeling and simulations in order to efficiently estimating their State-of-Charge (SoC) within MG systems. The work presented in this article focuses mainly on battery modeling for SoC estimation. We put more emphasize on battery system{\textquoteright}s characterization, modeling, SoC estimation and its integration into MG systems in order to study its performance. In fact, an instrumentation platform, composed of recent and low cost sensing and actuating equipment, is developed in order to identify the battery{\textquoteright}s parameters and then build the battery model. The simulation results show good agreement with the experimental results, and thus the battery model is validated in both charging and discharging processes. Then, the battery{\textquoteright}s SoC estimation in both charging and discharging processes is investigated by means of sophisticated methods, such as, Artificial Neural Network, Luenberger observer and Kalman Filter combined with Coulomb counting. The results of these methods are reported and compared to the SoC estimated using the Coulomb counting method in order to end up with the precise method. The modeled battery is afterwards integrated into an MG system, which is deployed into our EEBLab (Energy Efficient Building Laboratory) test-site, for simulation and experimental investigations. Finally, the four algorithms for SoC estimation are included into the instrumentation platform, which is connected to a Lead-acid battery already integrated into the MG system, in order to show and compare their performances and accuracy for online battery{\textquoteright}s SoC estimation. {\textcopyright} 2020 Elsevier Ltd},
	keywords = {Battery management systems, Battery state of charge, Charging (batteries), Coulomb counting method, Discharging process, energy efficiency, Energy efficient building, Experimental investigations, Intelligent buildings, Lead acid batteries, Luenberger observers, Model and simulation, Neural networks, Online systems, Sensing and actuating},
	doi = {10.1016/j.est.2020.101518},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085273724\&doi=10.1016\%2fj.est.2020.101518\&partnerID=40\&md5=f5d31d6e8a0f90e2bec4e1dd9d7da9d8},
	author = {Boulmrharj, S. and Ouladsine, R. and NaitMalek, Y. and Bakhouya, M. and Zine-Dine, K. and Khaidar, M. and Siniti, 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.}
}
@article {Lachhab2019276,
	title = {Context-driven monitoring and control of buildings ventilation systems using big data and Internet of Things{\textendash}based technologies},
	journal = {Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering},
	volume = {233},
	number = {3},
	year = {2019},
	note = {cited By 0},
	pages = {276-288},
	doi = {10.1177/0959651818791406},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052604424\&doi=10.1177\%2f0959651818791406\&partnerID=40\&md5=489ec0e147a47ea73d56d67443e20f71},
	author = {Lachhab, F. and Bakhouya, M. and Ouladsine, R. and Essaaidi, M.}
}
@article {Lachhab2019,
	title = {A context-driven platform using Internet of things and data stream processing for heating, ventilation and air conditioning systems control},
	journal = {Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering},
	year = {2019},
	note = {cited By 0},
	doi = {10.1177/0959651819841534},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064252643\&doi=10.1177\%2f0959651819841534\&partnerID=40\&md5=ce7ac91656b86854112d39b244314bab},
	author = {Lachhab, F. and Bakhouya, M. and Ouladsine, R. and Essaaidi, M.}
}
@conference {Boulmrharj201856,
	title = {Approach for dimensioning stand-alone photovoltaic systems},
	booktitle = {Energy Procedia},
	volume = {153},
	year = {2018},
	pages = {56-61},
	doi = {10.1016/j.egypro.2018.10.058},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057433465\&doi=10.1016\%2fj.egypro.2018.10.058\&partnerID=40\&md5=b03f45460b45700ebfe92eb2f1b70e53},
	author = {Boulmrharj, S. and NaitMalek, Y. and Elmouatamid, A. and Bakhouya, M. and Ouladsine, R. and Zine-Dine, K. and Khaidar, M. and Abid, R.}
}
@conference {Lachhab2018694,
	title = {A Context-Driven Approach using IoT and Big Data Technologies for Controlling HVAC Systems},
	booktitle = {2018 5th International Conference on Control, Decision and Information Technologies, CoDIT 2018},
	year = {2018},
	pages = {694-699},
	doi = {10.1109/CoDIT.2018.8394823},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050181848\&doi=10.1109\%2fCoDIT.2018.8394823\&partnerID=40\&md5=ff44ab22f61f614bb2aa03515fbbcdf9},
	author = {Lachhab, F. and Malek, Y.N. and Bakhouya, M. and Ouladsine, R. and Essaaidi, M.}
}
@conference {Lachhab2018,
	title = {An Energy-Efficient Approach for Controlling Heating and Air-Conditioning Systems},
	booktitle = {Proceedings of 2017 International Renewable and Sustainable Energy Conference, IRSEC 2017},
	year = {2018},
	doi = {10.1109/IRSEC.2017.8477265},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055866497\&doi=10.1109\%2fIRSEC.2017.8477265\&partnerID=40\&md5=ac2371ef07ae8fac3360b71a0343720f},
	author = {Lachhab, F. and Ouladsine, R. and Bakhouya, M. and Essaaidi, M.}
}
@conference {Boulmrharj2018,
	title = {Towards a battery characterization methodology for performance evaluation of micro-grid systems},
	booktitle = {2018 International Conference on Smart Energy Systems and Technologies, SEST 2018 - Proceedings},
	year = {2018},
	doi = {10.1109/SEST.2018.8495829},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056492547\&doi=10.1109\%2fSEST.2018.8495829\&partnerID=40\&md5=f25a82d795aa6991c23f1064013cde45},
	author = {Boulmrharj, S. and NaitMalek, Y. and Mouatamid, A.E. and Ouladsine, R. and Bakhouya, M. and Ouldmoussa, M. and Zine-Dine, K. and Khaidar, M. and Abid, R.}
}
@conference {Elmouatamid2018984,
	title = {Towards a Demand/Response Control Approach for Micro-grid Systems},
	booktitle = {2018 5th International Conference on Control, Decision and Information Technologies, CoDIT 2018},
	year = {2018},
	pages = {984-988},
	doi = {10.1109/CoDIT.2018.8394951},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050227456\&doi=10.1109\%2fCoDIT.2018.8394951\&partnerID=40\&md5=97a6b224a481de8a0d5d91a46b76b895},
	author = {Elmouatamid, A. and NaitMalek, Y. and Ouladsine, R. and Bakhouya, M. and Elkamoun, N. and Zine-Dine, K. and Khaidar, M. and Abid, R.}
}
@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.}
}
@conference {Lachhab2018926,
	title = {Towards an Intelligent Approach for Ventilation Systems Control using IoT and Big Data Technologies},
	booktitle = {Procedia Computer Science},
	volume = {130},
	year = {2018},
	pages = {926-931},
	doi = {10.1016/j.procs.2018.04.091},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051268549\&doi=10.1016\%2fj.procs.2018.04.091\&partnerID=40\&md5=7c925e4f04388d1fe39f849c5b0c02f1},
	author = {Lachhab, F. and Bakhouya, M. and Ouladsine, R. and Essaaidi, M.}
}
@article {Zbakh2017,
	title = {Cloud computing and big data: Technologies and applications},
	journal = {Concurrency Computation},
	volume = {29},
	number = {11},
	year = {2017},
	note = {cited By 0},
	doi = {10.1002/cpe.4090},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016746583\&doi=10.1002\%2fcpe.4090\&partnerID=40\&md5=9141a8028ba6f3210a8167cfdc5f9bc2},
	author = {Zbakh, M. and Bakhouya, M. and Essaaidi, M.}
}
@conference {Lachhab201764,
	title = {Performance evaluation of CEP engines for stream data processing},
	booktitle = {Proceedings of 2016 International Conference on Cloud Computing Technologies and Applications, CloudTech 2016},
	year = {2017},
	note = {cited By 2},
	pages = {64-69},
	abstract = {The easy deployment of wireless sensors allows the development of context-aware applications that could react to the environment changes and users{\textquoteright} preferences. For example, information extracted from data gathered using mobile phones and embedded computers in buses and taxis could be used to understand city dynamics in real-time and therefore take mitigation actions. However, gathering and real-time processing of relevant information is still a challenging task. Complex-event processing (CEP) techniques and predictive analytics have been recently proposed for analyzing streaming data in real-time in order to generate fast insights and then take suitable actions according to the environment changes. The work presented in this paper focuses mainly on the performance evaluation of three CEP engines widely used by researchers for semantic and physical streaming data processing. Experiments have been conducted using existing benchmark tools and results are reported to shed more light on the performance these engines for stream data processing. {\textcopyright} 2016 IEEE.},
	doi = {10.1109/CloudTech.2016.7847726},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013751287\&doi=10.1109\%2fCloudTech.2016.7847726\&partnerID=40\&md5=35e83758caf7164a4e2826c267fb217b},
	author = {Lachhab, F. and Bakhouya, M. and Ouladsine, R. and Essaaidi, M.}
}
