@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 {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 {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.}
}
@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 {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.}
}
