A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

Dana-Mihaela Petroșanu, George Căruțașu, Nicoleta Luminița Căruțașu and Alexandru Pîrjan
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Dana-Mihaela Petroșanu: Department of Mathematics-Informatics, Faculty of Applied Sciences, University Politehnica of Bucharest, Splaiul Independenței 313, 060042 Bucharest, Romania
George Căruțașu: Department of Informatics, Statistics and Mathematics, Romanian-American University, Expoziției 1B, 012101 Bucharest, Romania
Nicoleta Luminița Căruțașu: Department of Robotics and Production Systems, Faculty of Industrial Engineering and Robotics, University Politehnica of Bucharest, Splaiul Independenței 313, 060042 Bucharest, Romania
Alexandru Pîrjan: Department of Informatics, Statistics and Mathematics, Romanian-American University, Expoziției 1B, 012101 Bucharest, Romania
Authors registered in the RePEc Author Service: George Căruţaşu ()

Energies, 2019, vol. 12, issue 24, 1-64

Abstract: Lately, many scientists have focused their research on subjects like smart buildings, sensor devices, virtual sensing, buildings management, Internet of Things (IoT), artificial intelligence in the smart buildings sector, improving life quality within smart homes, assessing the occupancy status information, detecting human behavior with a view to assisted living, maintaining environmental health, and preserving natural resources. The main purpose of our review consists of surveying the current state of the art regarding the recent developments in integrating supervised and unsupervised machine learning models with sensor devices in the smart building sector with a view to attaining enhanced sensing, energy efficiency and optimal building management. We have devised the research methodology with a view to identifying, filtering, categorizing, and analyzing the most important and relevant scientific articles regarding the targeted topic. To this end, we have used reliable sources of scientific information, namely the Elsevier Scopus and the Clarivate Analytics Web of Science international databases, in order to assess the interest regarding the above-mentioned topic within the scientific literature. After processing the obtained papers, we finally obtained, on the basis of our devised methodology, a reliable, eloquent and representative pool of 146 papers scientific works that would be useful for developing our survey. Our approach provides a useful up-to-date overview for researchers from different fields, which can be helpful when submitting project proposals or when studying complex topics such those reviewed in this paper. Meanwhile, the current study offers scientists the possibility of identifying future research directions that have not yet been addressed in the scientific literature or improving the existing approaches based on the body of knowledge. Moreover, the conducted review creates the premises for identifying in the scientific literature the main purposes for integrating Machine Learning techniques with sensing devices in smart environments, as well as purposes that have not been investigated yet.

Keywords: internet of things; sensor networks; machine learning models; sensor devices; smart buildings; energy efficiency; optimal building management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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