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Neural network-based model predictive control system for optimizing building automation and management systems of sports facilities

Elnour, Mariam, Himeur, Yassine, Fadli, Fodil, Mohammedsherif, Hamdi, Ahmad, Ahmad M., Petri, Ioan ORCID:, Rezgui, Yacine ORCID: and Hodorog, Andrei ORCID: 2022. Neural network-based model predictive control system for optimizing building automation and management systems of sports facilities. Applied Energy 318 , pp. 119-153. 10.1016/j.apenergy.2022.119153

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Sports 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), -nearest neighbor (NN), 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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Publisher: Elsevier
ISSN: 0306-2619
Funders: Sporte.3Q NPRP grant No. NPRP12S-0222-190128 from the Qatar National Research Fund (a member of Qatar Foundation)
Date of First Compliant Deposit: 12 May 2022
Date of Acceptance: 14 April 2022
Last Modified: 07 Nov 2023 11:07

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