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AuthorElnour, Mariam
AuthorFadli, Fodil
AuthorMeskin, Nader
AuthorPetri, Ioan
AuthorRezgui, Yacine
Available date2024-03-18T08:38:51Z
Publication Date2023-02
Publication NameACM International Conference Proceeding Series
Identifierhttp://dx.doi.org/10.1145/3587716.3587749
CitationElnour, M., Fadli, F., Meskin, N., Petri, I., & Rezgui, Y. (2023, February). Analysis of unsupervised consumption anomaly detection in sports facilities using artificial intelligence-based data analytics: A case study. In Proceedings of the 2023 15th International Conference on Machine Learning and Computing (pp. 197-204).
ISBN978-145039841-1
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85173845784&origin=inward
URIhttp://hdl.handle.net/10576/53145
AbstractSports facilities have exceptionally high energy demand due to the extensive operational requirements and high-occupancy seasonal rates. Towards promoting efficient energy usage and minimal losses, consumption anomaly detection in sports facilities is addressed in this work using Artificial intelligence (AI)-based analytics approaches. Traditional AI-based data analytics approaches are applied in a practical context for a local sports complex. The actual unlabeled operation data of the facility are used and a case-specific comparative analysis of the various approaches is presented where AI-based data labeling is used. The characteristics of the different algorithms are contextually discussed. It was found that the size and distribution of the training datasets influence the performance of the different algorithms. This study represents preliminary findings on the topic with a promising potential for further research.
SponsorThis publication was made possible by NPRP grant No. NPRP12S-0222-190128.
Languageen
PublisherAssociation for Computing Machinery (ACM)
Subjectanomaly detection
artificial intelligence
data analytics
Machine learning
sports facility
TitleAnalysis of Unsupervised Consumption Anomaly Detection in Sports Facilities using Artificial Intelligence-Based Data Analytics: A Case Study
TypeConference Paper
Pagination197-204
dc.accessType Full Text


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