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المؤلفElnour, M.
المؤلفHimeur, Yassine
المؤلفFadli, Fodil
المؤلفMohammedsherif, Hamdi
المؤلفMeskin, Nader
المؤلفAhmad, Ahmad M.
المؤلفPetri, Ioan
المؤلفRezgui, Yacine
المؤلفHodorog, Andrei
تاريخ الإتاحة2023-03-30T07:18:06Z
تاريخ النشر2022-07-15
اسم المنشورApplied Energy
المعرّفhttp://dx.doi.org/10.1016/j.apenergy.2022.119153
الاقتباسElnour, M., Himeur, Y., Fadli, F., Mohammedsherif, H., Meskin, N., Ahmad, A. M., ... & Hodorog, A. (2022). Neural network-based model predictive control system for optimizing building automation and management systems of sports facilities. Applied Energy, 318, 119153.‏
الرقم المعياري الدولي للكتاب03062619
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85129471303&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/41512
الملخصSports facilities are considered complex buildings due to their high energy demand and occupancy profiles. Therefore, their management and optimization are crucial for reducing their energy consumption and carbon footprint while maintaining an appropriate indoor environmental quality. This work is part of the SportE3.Q project, which aims to manage and optimize the operation of sports facilities. A neural network (NN)-based model predictive control (MPC) management and optimization system is proposed for the heating, ventilation, and air conditioning (HVAC) system of a sports hall in the sports and events complex of Qatar University (QU). The proposed approach provides an integrated dynamic optimization method that accounts for future system behavior in the decision-making process, consisting of a prediction element and an optimizer. A NN is used to implement the dynamic prediction element of the MPC system and is compared with other machine learning (ML)-based models, which are support vector regression (SVR), k-nearest neighbor (kNN), and decision trees (DT). The NN-based model outperforms the other ML models with an average root mean squared error (RMSE) of around 0.06 between the actual and the predicted values, and an average R of 0.99 as NNs are popular for their high accuracy and reliability. Two schemes of the proposed NN-based MPC system are investigated for managing and optimizing the operation of the hall's HVAC system for enhanced energy use and indoor environment quality, as well as for providing occupancy profile recommendations to aid the facilities’ managers in handling their operation. In alignment with the objective of the SportE3.Q project, up to 46% energy reduction was achieved while jointly optimizing the thermal comfort and indoor air quality. In addition, Scheme 2 of the proposed system provided productive occupancy recommendations for a healthier indoor environment.
راعي المشروعThis publication was made possible by NPRP grant No. NPRP12S-0222-190128 from the Qatar National Research Fund (a member of Qatar Foundation).
اللغةen
الناشرElsevier Ltd
الموضوعEnergy management and optimization
Model predictive control
Neural networks
Sports facilities
العنوانNeural network-based model predictive control system for optimizing building automation and management systems of sports facilities
النوعArticle
الصفحات119-153
رقم المجلد318
dc.accessType Abstract Only


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