A comprehensive review of approaches to building occupancy detection. (August 2020)

• Record Type: Journal Article
• Authors: Rueda, Luis; Agbossou, Kodjo; Cardenas, Alben; Henao, Nilson; Kelouwani, Sousso
• Is Part Of: Building and environment. Volume 180(2020)
• Publication Date: 2020-08
• Subjects: Occupancy detection -- Analytical methods -- Data-driven methods -- Knowledge-based methods -- Sensor; Buildings -- Environmental engineering -- Periodicals; Building -- Research -- Periodicals; Constructions -- Technique de l'environnement -- Périodiques; Electronic journals; 696
• DOI: 10.1016/j.buildenv.2020.106966
• Languages: English
• ISSNs: 0360-1323

Abstract

Abstract: Detailed occupancy information in buildings is useful to improve the performance of energy management systems in order to enable energy consumption savings and maintain occupants' comfort. Different technologies employed to provide occupancy information account for high-precision devices such as optical and thermal cameras, and environmental or specialized sensors like carbon dioxide (CO2 ) and passive infrared (PIR). Although the latter systems have lower accuracy, they have received significant interest due to their affordable and less-intrusive nature. Accordingly, various studies have been conducted to explore the various elements of these technologies. Nevertheless, the algorithmic aspect of the occupancy detection process has not been adequately taken into consideration. This paper presents an extensive review of the techniques that have been exploited to process the information provided by the sensors and carry out occupancy information detection. In this study, a complete set of comparison criteria, comprising the performance, the occupancy resolution, the type of sensors used, the type of buildings, and the energy saving potentials has been considered in order to perform an in-depth analysis of the occupancy detection systems. Through its examination, this paper elaborates significant remarks on occupancy detection algorithms in order to realize a method that is not only efficient in processing sensors' data but also effective in providing accurate occupancy information. Highlights: A comprehensive review of the techniques that have been exploited to estimate building occupancy is presented. Building occupancy detection techniques were categorized as analytical, data-driven, and knowledge-based methods. A comparison framework to help readers to better benchmark occupancy detection systems is presented. Some potential future research directions are discussed.