Show simple item record

AuthorHimeur, Yassine
AuthorElnour, Mariam
AuthorFadli, Fodil
AuthorMeskin, Nader
AuthorPetri, Ioan
AuthorRezgui, Yacine
AuthorBensaali, Faycal
AuthorAmira, Abbes
Available date2022-12-29T07:34:41Z
Publication Date2022
Publication NameArtificial Intelligence Review
ResourceScopus
URIhttp://dx.doi.org/10.1007/s10462-022-10286-2
URIhttp://hdl.handle.net/10576/37799
AbstractIn theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings' performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings' management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings' performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings. 2022, The Author(s).
SponsorThis publication was made possible by NPRP Grant No. NPRP12S-0222-190128 from the Qatar National Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the authors.
Languageen
PublisherSpringer Nature
SubjectArtificial intelligence
Big data analytics
Building automation and management system
Computing platforms
Deep learning
Evaluation metrics
TitleAI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives
TypeArticle
dc.accessType Open Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record