@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 {Saidi2022v, title = {Preface}, journal = {Lecture Notes on Data Engineering and Communications Technologies}, volume = {110}, year = {2022}, note = {cited By 0}, pages = {v-vi}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123895200\&partnerID=40\&md5=0b0333a3d7c88e9c46183a1273df88c7}, author = {Saidi, R. and El Bhiri, B. and Maleh, Y. and Mosallam, A. 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.} } @conference {Essaaidi2020, title = {Preface}, booktitle = {Proceedings of 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications, CloudTech 2020}, year = {2020}, note = {cited By 0}, doi = {10.1109/CloudTech49835.2020.9365910}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102619681\&doi=10.1109\%2fCloudTech49835.2020.9365910\&partnerID=40\&md5=3c74b02844a103a62a82dab03f17563f}, author = {Essaaidi, M. and Zbakh, M. and Ouacha, A.} } @conference {Essaaidi20181, title = {Preface}, booktitle = {Proceedings of 2017 International Conference of Cloud Computing Technologies and Applications, CloudTech 2017}, volume = {2018-January}, year = {2018}, pages = {1}, doi = {10.1109/CloudTech.2017.8284695}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046638075\&doi=10.1109\%2fCloudTech.2017.8284695\&partnerID=40\&md5=d1991fc2f2164bd18b0d514a9e640661}, author = {Essaaidi, M. and Zbakh, 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.} } @conference {Essaaidi2017, title = {Preface}, booktitle = {Proceedings of 2016 International Conference on Cloud Computing Technologies and Applications, CloudTech 2016}, year = {2017}, note = {cited By 0}, doi = {10.1109/CloudTech.2016.7847678}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013815065\&doi=10.1109\%2fCloudTech.2016.7847678\&partnerID=40\&md5=afb7995783f787b1355795e838f8e423}, author = {Essaaidi, M. and Zbakh, M.} } @conference {Essaaidi2015, title = {Preface}, booktitle = {Proceedings of 2015 International Conference on Cloud Computing Technologies and Applications, CloudTech 2015}, year = {2015}, note = {cited By 0}, doi = {10.1109/CloudTech.2015.7336961}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962888037\&doi=10.1109\%2fCloudTech.2015.7336961\&partnerID=40\&md5=69249a453813373a5128d84c6cbea760}, author = {Essaaidi, M. and Zbakh, M.} } @conference {Essaaidi2012, title = {Proceedings of 2012 International Conference on Multimedia Computing and Systems, ICMCS 2012: Foreword}, booktitle = {Proceedings of 2012 International Conference on Multimedia Computing and Systems, ICMCS 2012}, year = {2012}, note = {cited By 0}, doi = {10.1109/ICMCS.2012.6320113}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84869744137\&doi=10.1109\%2fICMCS.2012.6320113\&partnerID=40\&md5=8ef3c1358ee74f9233c3c4c6f9f7f9e4}, author = {Essaaidi, M. and Zaz, Y.} }