Next Article in Journal
An Obstacle Detection Method Based on Longitudinal Active Vision
Previous Article in Journal
Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks
Previous Article in Special Issue
Thermal-Adaptation-Behavior-Based Thermal Sensation Evaluation Model with Surveillance Cameras
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Systematic Review

Smart Buildings: A Comprehensive Systematic Literature Review on Data-Driven Building Management Systems

by
Adrian Taboada-Orozco
1,
Kokou Yetongnon
2 and
Christophe Nicolle
2,*
1
K.I.D.S A.I’S, 14 Rue du Golf, 21800 Quetigny, France
2
CIAD Laboratory, University of Burgundy, 21000 Dijon, France
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(13), 4405; https://doi.org/10.3390/s24134405 (registering DOI)
Submission received: 30 April 2024 / Revised: 13 June 2024 / Accepted: 5 July 2024 / Published: 7 July 2024
(This article belongs to the Special Issue Artificial Intelligence and Sensors in Smart Buildings)

Abstract

Buildings are complex structures composed of heterogeneous elements; these require building management systems (BMSs) to dynamically adapt them to occupants’ needs and leverage building resources. The fast growth of information and communication technologies (ICTs) has transformed the BMS field into a multidisciplinary one. Consequently, this has caused several research papers on data-driven solutions to require examination and classification. This paper provides a broad overview of BMS by conducting a systematic literature review (SLR) summarizing current trends in this field. Unlike similar reviews, this SLR provides a rigorous methodology to review current research from a computer science perspective. Therefore, our goal is four-fold: (i) Identify the main topics in the field of building; (ii) Identify the recent data-driven methods; (iii) Understand the BMS’s underlying computing architecture (iv) Understand the features of BMS that contribute to the smartization of buildings. The result synthesizes our findings and provides research directions for further research.
Keywords: building management systems; systematic literature review; buildings; smart buildings; computer science building management systems; systematic literature review; buildings; smart buildings; computer science

Share and Cite

MDPI and ACS Style

Taboada-Orozco, A.; Yetongnon, K.; Nicolle, C. Smart Buildings: A Comprehensive Systematic Literature Review on Data-Driven Building Management Systems. Sensors 2024, 24, 4405. https://doi.org/10.3390/s24134405

AMA Style

Taboada-Orozco A, Yetongnon K, Nicolle C. Smart Buildings: A Comprehensive Systematic Literature Review on Data-Driven Building Management Systems. Sensors. 2024; 24(13):4405. https://doi.org/10.3390/s24134405

Chicago/Turabian Style

Taboada-Orozco, Adrian, Kokou Yetongnon, and Christophe Nicolle. 2024. "Smart Buildings: A Comprehensive Systematic Literature Review on Data-Driven Building Management Systems" Sensors 24, no. 13: 4405. https://doi.org/10.3390/s24134405

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop