CO2 based occupancy detection algorithm: Experimental analysis and validation for office and residential buildings. (April 2015)

• Record Type: Journal Article
• Authors: Calì, Davide; Matthes, Peter; Huchtemann, Kristian; Streblow, Rita; Müller, Dirk
• Is Part Of: Building and environment. Volume 86(2015)
• Page Start: 39
• Page End: 49
• Publication Date: 2015-04
• Subjects: Occupants detection -- Presence detection -- Carbon dioxide CO2 -- User behavior -- Ventilation -- Case study; Buildings -- Environmental engineering -- Periodicals; Building -- Research -- Periodicals; Constructions -- Technique de l'environnement -- Périodiques; Electronic journals; 696
• DOI: 10.1016/j.buildenv.2014.12.011
• Languages: English
• ISSNs: 0360-1323

Abstract

Abstract: The detection of occupants in indoors can be fundamental for a correct operation of the installed engineering systems (e.g. lighting, ventilation, heating and cooling). Further, real occupancy profiles can be used as input of stochastic models for dynamic simulation of buildings and their engineering systems. In this work an algorithm for the detection of occupants in the indoor environment is presented, validated and evaluated among different scenarios. The algorithm is based on the concentration of carbon dioxide in the indoor air. The testing and validation has been done both for residential and non-residential buildings: two offices with mechanical ventilation system, one office without mechanical ventilation, a kitchen and a big sleeping/living room of a residential building without mechanical ventilation have been evaluated. Volunteers recorded their presence profiles in the monitored rooms to permit the validation of the algorithms. The results of the algorithms for the detection of occupants (whether occupants are present or not) provides correct presence profile up to 95.8% of the time while the exact number of occupants in the rooms is correctly identified up to 80.6% of the time. Highlights: An algorithm for presence detection in indoor spaces has been developed & validated. The algorithm uses the indoor air CO2 concentration to detect presence. The algorithm has been tested and validated both for residential & office buildings. The general presence of occupants is detected correctly up to 96% of the time. The exact number of occupants is detected correctly up to 81% of the time.