The new directive 2018/844/EU on Energy Performance of Buildings and
Energy Efficiency, supports the change of building into smarter, more energy-efficient
and include the perspective of the occupants' needs. The knowledge of occupant
behaviour is the centre of the balance between the buildings energy efficiency and its
indoor environmental quality. This chapter presents a state-of-the-art of the buildings
occupant behaviour, presenting the new developments and future trends. It summarises
research in which a series of methodologies were developed to supply relevant data to
the building management systems (BMS). These methodologies use a monitoring
system based on environmental sensors, namely relative humidity, temperature, and
carbon dioxide. New methods to detect the occupant actions in the operation of
building systems were summarised and compared. The methodologies were based on
statistical tools and machine learning techniques. They can be applied to different case
studies since they can adapt to the local environment under a self-learning strategy. The
drivers of behaviour for the operation of those building systems were also analysed.
Two methodologies that allowed to predict the occupant actions taking into account the
parameters that influenced the occupant behaviour were described. It was also possible
establishing the seasonality of drivers of behaviour. The overall results highlight that
the actions, motivations, and impacts of a specific set of occupants performed in
building systems can significantly vary depending on the room and on environmental
parameters.
Keywords: Action detection, Drivers of behavior, Intelligent buildings, In situ
data acquisition, Occupant behaviour.
