###### Abstract
[17] found that the preferred types of energy-saving measures alter between individuals with different environmental concerns. The average willingness to pay for improving energy performance varied among people according to their age, family status and economic welfare. [13]. In addition, occupants' attitudes towards different levels of automation in domestic control systems were found, in another study, to differ, as full automation is not always suitable for systems that considerably affect indoor environmental comfort [14]; research reveals diverging approaches as regards the frequency or probability of user control actions [6; 25]. Thus, it comes as no surprise that indoor temperature setpoints may largely differ due to occupant preferences and related behaviour. [PERSON] et al. [15] suggested that energy savings due to conservation measures will be lower in practice than those calculated, due to the neglect of the impact of consumer behaviour. Additionally, researches have shown that occupant behaviour is associated with certain types of building characteristics [17] and heating and ventilation systems, identifying at the same time an important rebound effect in energy consumption, i.e., a tendency to use more of an optimised resource [13; 17; 26; 27]. In recent years, several models have been established to integrate the influence of buildings' occupant behaviour into building simulation programs [6; 19]. However, deviations between predicted and actual performance \begin{table}
\begin{tabular}{c c c} \hline \hline
**Energy Use** & **Commercial Buildings** & **Residential Buildings** \\ \hline Space heating and cooling & 32\% & 41\% \\ Hot water & 6\% & 13\% \\ Lighting & 25\% & 7\% \\ Cooking and Refrigeration & 6\% & 9\% \\ Other electric amenities & 25\% & 21\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Energy use (source [21]). remained significant [16]. Some researchers [28] have considered occupants' behaviour as binary: heating on or off. A study by [PERSON] et al. [1] tried to identify the possibility of energy consumption in apartment buildings, regarding the fluctuation of real energy consumption [1]. These variations revealed that deterministic values of typical building conditions could provide an interpretation of energy consumption in these buildings [1]. To get the full effect of user behaviour, one possible approach would be to extract corresponding useful information from actual measured data, since these data already contain all the effects [19] and could prompt lower operation related to energy behaviour [7]. Behaviour and behavioural patterns impacting building energy performance pertain to the building operation phase; they typically relate to adjusting thermostat settings, opening or closing windows, dimming or switching lights, pulling blinds up or down, turning heating on or off, Heating, Ventilation, and Air Conditioning (HVAC) systems, movement between spaces [6; 27], etc. Adequate promotion of an energy-conscious behaviour could minimise electricity consumption by more than 10% [19]. In addition, in some cases, with the incorporation of new technologies occupants are faced with complex systems that are difficult to operate, something which can lead to a further increase in energy use [26]. Additionally, even though the use of low carbon technologies promises a good indoor environment and low energy consumption for the building occupants, there are still uncertainties related to their payback periods [29]. Occupant behaviour patterns are usually divided into occupant movement and presence models, and action models. Action models encompasses various types of (adaptive) behaviour, such as adjusting blinds and windows, switching lights on/off, using appliances, setting the thermostat, etc. A comprehensive occupant behaviour model thus includes a series of sub-models which can be classified according to their complexity. As current practice in modelling occupant behaviour is varied, a great number of increasingly complex models are being developed for many activities.