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

• Published in: Renewable & sustainable energy reviews Vol. 162; p. 112401
• Main Authors: Elnour, Mariam, Fadli, Fodil, Himeur, Yassine, Petri, Ioan, Rezgui, Yacine, Meskin, Nader, Ahmad, Ahmad M.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2022
• Subjects: Artificial intelligence; Building automation and management system (BAMS); Dynamic simulation; Energy optimization and management; Optimization; Sustainable smart sports facility; Dynamic simulation; Energy optimization and management; Building automation and management system (BAMS); Artificial intelligence; Sustainable smart sports facility; Optimization
• ISSN: 1364-0321; 1879-0690
• DOI: 10.1016/j.rser.2022.112401

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. [Display omitted] •Promoting efficient and sustainable sports facilities for green and socially resilient cities.•Addressed the research gap in the management of sports facilities compared to other buildings.•Emphasized the importance of the regional context in the operation management of sports facilities.•71% of the studies are for managing and optimizing sports. facilities located in cold regions•Presented the limitations of existing works and the prospects in the topic under study.