ESTIMATION OF OCCUPANCY STATUS AND LEVELS FOR INDOOR SPACES

Abstract

Building energy use today accounts for over 40% of total primary energy consumption. The energy demand for buildings can be decreased through efficient building and facility management. The knowledge related to the use of indoor spaces is a key to successful management. The research aimed to investigate whether the occupancy levels of an indoor space can automatically be determined via machine learning algorithms based on data acquired from multiple indoor sensors. The study involved indoor data collection and a machine learning experiment. The results indicated that machine learning can be considered as a promising approach for the detection of indoor occupancy status and levels.