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Performance and energy optimization of building automation and management systems: Towards smart sustainable carbon-neutral sports facilities

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  • Elnour, Mariam
  • Fadli, Fodil
  • Himeur, Yassine
  • Petri, Ioan
  • Rezgui, Yacine
  • Meskin, Nader
  • Ahmad, Ahmad M.

Abstract

Sports facilities (SFs) consume massive energy given their unique demand profiles and operation requirements. Intelligent and effective solutions are necessary to tackle the matter of facilities’ sustainability and efficiency. Promoting efficient and environmentally sustainable SFs is pivotal towards environmentally friendly and socially resilient cities. There is a lack of systematic literature review focused on the progress in SFs operation, sustainability, and energy optimization. To the best of the authors’ knowledge, this research presents the first review article addressing the research gap in building operation management and optimization for SFs compared to other types of buildings and SFs located in hot climatic zones compared to cold ones. The topic’s significance is highlighted with emphasis on the climatic zone and the characteristics of SFs. A comprehensive review and in-depth discussion of existing solutions are presented. Limited studies covered the management and optimization of SFs for the past five years compared to residential and commercial buildings, and 71% of them were for facilities located in cold regions. About 45% of the surveyed works targeted swimming pools since they are the most popular SFs’ type with the highest energy consumption per usable area. 39% of the reviewed studies employed simulation-based approaches to investigate the subject, 26% used artificial intelligence and machine learning, and 35% utilized optimization algorithms and other standard approaches. The limitations of those works and the prospects in energy and operation optimization of SFs are presented. The latter includes deploying evolving typologies such as deep learning, developing modular solutions that can be integrated into existing technologies, and deploying renewable energy systems for sustainable facilities. Finally, the active role of SFs in energy markets is discussed.

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  • Elnour, Mariam & Fadli, Fodil & Himeur, Yassine & Petri, Ioan & Rezgui, Yacine & Meskin, Nader & Ahmad, Ahmad M., 2022. "Performance and energy optimization of building automation and management systems: Towards smart sustainable carbon-neutral sports facilities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    • Handle: RePEc:eee:rensus:v:162:y:2022:i:c:s1364032122003100
    DOI: 10.1016/j.rser.2022.112401
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    as
    1. Konstantakopoulos, Ioannis C. & Barkan, Andrew R. & He, Shiying & Veeravalli, Tanya & Liu, Huihan & Spanos, Costas, 2019. "A deep learning and gamification approach to improving human-building interaction and energy efficiency in smart infrastructure," Applied Energy, Elsevier, vol. 237(C), pages 810-821.
    2. Eunil Park & Sang Jib Kwon & Angel P. Del Pobil, 2016. "For a Green Stadium: Economic Feasibility of Sustainable Renewable Electricity Generation at the Jeju World Cup Venue," Sustainability, MDPI, vol. 8(10), pages 1-11, September.
    3. Lü, Xiaoshu & Lu, Tao & Kibert, Charles J. & Viljanen, Martti, 2015. "Modeling and forecasting energy consumption for heterogeneous buildings using a physical–statistical approach," Applied Energy, Elsevier, vol. 144(C), pages 261-275.
    4. Nord, Natasa & Mathisen, Hans Martin & Cao, Guangyu, 2015. "Energy cost models for air supported sports hall in cold climates considering energy efficiency," Renewable Energy, Elsevier, vol. 84(C), pages 56-64.
    5. Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
    6. Himeur, Yassine & Ghanem, Khalida & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2021. "Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives," Applied Energy, Elsevier, vol. 287(C).
    7. Song, Chunhe & Jing, Wei & Zeng, Peng & Yu, Haibin & Rosenberg, Catherine, 2018. "Energy consumption analysis of residential swimming pools for peak load shaving," Applied Energy, Elsevier, vol. 220(C), pages 176-191.
    8. Iria, José & Soares, Filipe & Matos, Manuel, 2019. "Optimal bidding strategy for an aggregator of prosumers in energy and secondary reserve markets," Applied Energy, Elsevier, vol. 238(C), pages 1361-1372.
    9. Li, Yantong & Ding, Zhixiong & Du, Yaxing, 2020. "Techno-economic optimization of open-air swimming pool heating system with PCM storage tank for winter applications," Renewable Energy, Elsevier, vol. 150(C), pages 878-890.
    10. Ioan Petri & Sylvain Kubicki & Yacine Rezgui & Annie Guerriero & Haijiang Li, 2017. "Optimizing Energy Efficiency in Operating Built Environment Assets through Building Information Modeling: A Case Study," Energies, MDPI, vol. 10(8), pages 1-17, August.
    11. Yu, Yanzhe & You, Shijun & Zhang, Huan & Ye, Tianzhen & Wang, Yaran & Wei, Shen, 2021. "A review on available energy saving strategies for heating, ventilation and air conditioning in underground metro stations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    12. Zhen Liu & Ziyuan Chi & Mohamed Osmani & Peter Demian, 2021. "Blockchain and Building Information Management (BIM) for Sustainable Building Development within the Context of Smart Cities," Sustainability, MDPI, vol. 13(4), pages 1-17, February.
    13. Mustafa Alparslan Zehir & Kadir Baris Ortac & Hakan Gul & Alp Batman & Zafer Aydin & João Carlos Portela & Filipe Joel Soares & Mustafa Bagriyanik & Unal Kucuk & Aydogan Ozdemir, 2019. "Development and Field Demonstration of a Gamified Residential Demand Management Platform Compatible with Smart Meters and Building Automation Systems," Energies, MDPI, vol. 12(5), pages 1-18, March.
    14. Ioan Petri & Omer Rana & Yacine Rezgui & Fodil Fadli, 2021. "Edge HVAC Analytics," Energies, MDPI, vol. 14(17), pages 1-11, September.
    15. De Boeck, L. & Verbeke, S. & Audenaert, A. & De Mesmaeker, L., 2015. "Improving the energy performance of residential buildings: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 960-975.
    16. Antonio Paone & Jean-Philippe Bacher, 2018. "The Impact of Building Occupant Behavior on Energy Efficiency and Methods to Influence It: A Review of the State of the Art," Energies, MDPI, vol. 11(4), pages 1-19, April.
    17. Liu, Zhijian & Liu, Yuanwei & He, Bao-Jie & Xu, Wei & Jin, Guangya & Zhang, Xutao, 2019. "Application and suitability analysis of the key technologies in nearly zero energy buildings in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 329-345.
    18. Yang, Lei & Nagy, Zoltan & Goffin, Philippe & Schlueter, Arno, 2015. "Reinforcement learning for optimal control of low exergy buildings," Applied Energy, Elsevier, vol. 156(C), pages 577-586.
    19. Grzegorz Majewski & Łukasz J. Orman & Marek Telejko & Norbert Radek & Jacek Pietraszek & Agata Dudek, 2020. "Assessment of Thermal Comfort in the Intelligent Buildings in View of Providing High Quality Indoor Environment," Energies, MDPI, vol. 13(8), pages 1-20, April.
    20. Dimitris Al. Katsaprakakis, 2020. "Computational Simulation and Dimensioning of Solar-Combi Systems for Large-Size Sports Facilities: A Case Study for the Pancretan Stadium, Crete, Greece," Energies, MDPI, vol. 13(9), pages 1-30, May.
    21. Fadzli Haniff, Mohamad & Selamat, Hazlina & Yusof, Rubiyah & Buyamin, Salinda & Sham Ismail, Fatimah, 2013. "Review of HVAC scheduling techniques for buildings towards energy-efficient and cost-effective operations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 94-103.
    22. Wei, Wu & Skye, Harrison M., 2021. "Residential net-zero energy buildings: Review and perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    23. Piotr Ciuman & Jan Kaczmarczyk, 2021. "Numerical Analysis of the Energy Consumption of Ventilation Processes in the School Swimming Pool," Energies, MDPI, vol. 14(4), pages 1-18, February.
    24. Hyun-Jung Yoon & Dong-Seok Lee & Hyun Cho & Jae-Hun Jo, 2018. "Prediction of Thermal Environment in a Large Space Using Artificial Neural Network," Energies, MDPI, vol. 11(2), pages 1-15, February.
    25. Halhoul Merabet, Ghezlane & Essaaidi, Mohamed & Ben Haddou, Mohamed & Qolomany, Basheer & Qadir, Junaid & Anan, Muhammad & Al-Fuqaha, Ala & Abid, Mohamed Riduan & Benhaddou, Driss, 2021. "Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    26. Mattia Manni & Valentina Coccia & Andrea Nicolini & Guido Marseglia & Alessandro Petrozzi, 2018. "Towards Zero Energy Stadiums: The Case Study of the Dacia Arena in Udine, Italy," Energies, MDPI, vol. 11(9), pages 1-16, September.
    27. Petri, Ioan & Li, Haijiang & Rezgui, Yacine & Chunfeng, Yang & Yuce, Baris & Jayan, Bejay, 2014. "A modular optimisation model for reducing energy consumption in large scale building facilities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 990-1002.
    28. Andoni, Merlinda & Robu, Valentin & Flynn, David & Abram, Simone & Geach, Dale & Jenkins, David & McCallum, Peter & Peacock, Andrew, 2019. "Blockchain technology in the energy sector: A systematic review of challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 143-174.
    29. Pomianowski, M.Z. & Johra, H. & Marszal-Pomianowska, A. & Zhang, C., 2020. "Sustainable and energy-efficient domestic hot water systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 128(C).
    30. Johnson, Daniel & Horton, Ella & Mulcahy, Rory & Foth, Marcus, 2017. "Gamification and serious games within the domain of domestic energy consumption: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 249-264.
    31. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Effective non-intrusive load monitoring of buildings based on a novel multi-descriptor fusion with dimensionality reduction," Applied Energy, Elsevier, vol. 279(C).
    32. Afroz, Zakia & Shafiullah, GM & Urmee, Tania & Higgins, Gary, 2018. "Modeling techniques used in building HVAC control systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 64-84.
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