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AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

In 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...

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Autores principales: Himeur, Yassine, Elnour, Mariam, Fadli, Fodil, Meskin, Nader, Petri, Ioan, Rezgui, Yacine, Bensaali, Faycal, Amira, Abbes
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9568938/
https://www.ncbi.nlm.nih.gov/pubmed/36268476
http://dx.doi.org/10.1007/s10462-022-10286-2
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author Himeur, Yassine
Elnour, Mariam
Fadli, Fodil
Meskin, Nader
Petri, Ioan
Rezgui, Yacine
Bensaali, Faycal
Amira, Abbes
author_facet Himeur, Yassine
Elnour, Mariam
Fadli, Fodil
Meskin, Nader
Petri, Ioan
Rezgui, Yacine
Bensaali, Faycal
Amira, Abbes
author_sort Himeur, Yassine
collection PubMed
description In 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.
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spelling pubmed-95689382022-10-16 AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives Himeur, Yassine Elnour, Mariam Fadli, Fodil Meskin, Nader Petri, Ioan Rezgui, Yacine Bensaali, Faycal Amira, Abbes Artif Intell Rev Article In 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. Springer Netherlands 2022-10-15 2023 /pmc/articles/PMC9568938/ /pubmed/36268476 http://dx.doi.org/10.1007/s10462-022-10286-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Himeur, Yassine
Elnour, Mariam
Fadli, Fodil
Meskin, Nader
Petri, Ioan
Rezgui, Yacine
Bensaali, Faycal
Amira, Abbes
AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives
title AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives
title_full AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives
title_fullStr AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives
title_full_unstemmed AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives
title_short AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives
title_sort ai-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9568938/
https://www.ncbi.nlm.nih.gov/pubmed/36268476
http://dx.doi.org/10.1007/s10462-022-10286-2
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