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AuthorElnour, M.
AuthorHimeur, Yassine
AuthorFadli, Fodil
AuthorMohammedsherif, Hamdi
AuthorMeskin, Nader
AuthorAhmad, Ahmad M.
AuthorPetri, Ioan
AuthorRezgui, Yacine
AuthorHodorog, Andrei
Available date2023-03-30T07:18:06Z
Publication Date2022-07-15
Publication NameApplied Energy
Identifierhttp://dx.doi.org/10.1016/j.apenergy.2022.119153
CitationElnour, 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.‏
ISSN03062619
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85129471303&origin=inward
URIhttp://hdl.handle.net/10576/41512
AbstractSports 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.
SponsorThis publication was made possible by NPRP grant No. NPRP12S-0222-190128 from the Qatar National Research Fund (a member of Qatar Foundation).
Languageen
PublisherElsevier Ltd
SubjectEnergy management and optimization
Model predictive control
Neural networks
Sports facilities
TitleNeural network-based model predictive control system for optimizing building automation and management systems of sports facilities
TypeArticle
Pagination119-153
Volume Number318
dc.accessType Abstract Only


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