@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 {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 {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 {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.} } @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.} }