A MACHINE LEARNING APPROACH ON OCCUPANT NUMBER PREDICTION FOR INDOOR SPACES

Isikdag, U.; Sahin, K.; Cansiz, S.

The knowledge about the occupancy of an indoor space can serve to various domains ranging from emergency response to energy efficiency in buildings. The literature in the field presents various methods for occupancy detection. Data gathered for occupancy detection, can also be used to predict the number of occupants at a certain indoor space and time. The aim of this research was to determine the number of occupants in an indoor space, through the utilisation of information acquired from a set of sensors and machine learning techniques. The sensor types used in this research was a sound level sensor, temperature/humidity level sensor and an air quality level sensor. Based on data acquired from these sensors six automatic classification techniques are employed and tested with the aim of automatically detecting the number of occupants in an indoor space by making use of multi-sensor information. The results of the tests demonstrated that machine learning techniques can be used as a tool for prediction of number of occupants in an indoor space.

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Isikdag, U. / Sahin, K. / Cansiz, S.: A MACHINE LEARNING APPROACH ON OCCUPANT NUMBER PREDICTION FOR INDOOR SPACES. 2018. Copernicus Publications.

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