## 2 Concept and Benefits  
The work and results reported in this paper were part of the work-plan of the HORIZON 2020 HIT2 GAP project [31]. Within this project, a specific module (Behaviour Module; Events BMEs) was developed and tested in the Challenger office building, owned by Bouygues and located in Versailles, France.  
The aim of the BME is to track, assess and report upon behavioural events to specific, registered building users. Behaviour, in the BME context, is considered in terms of events, i.e., specific instances and situations that manifest some problematic user behaviour, mainly in terms of energy efficiency. This said, in some cases, the event may, conversely, be corresponding to an energy-saving practice. This is again recorded, assessed and reported upon.  
Notifications can be set for various users, typically building users and facility managers although other parties (e.g., external maintenance service providers) can also be set up as event information recipients.  
At the moment, the following events have been considered and their calculation methods have been defined, although not all of them have been practically tested.  
* Off-hours events, whereby the HVAC, lighting or plug consumption is monitored during off-hours when they normally should be zero or close to zero. The event is triggered when these consumptions are traced to be higher than some threshold values, i.e., consumption that is considered normal for off-hours.
* Setpoint deviation events, whereby the actual space temperature is tracked and its deviation from the setpoint is recorded. A slack, corresponding to an acceptable deviation, is allowed by design and the event is triggered whenever the temperature exceeds the setpoint plus the slack setting. Obviously, the space temperature must be so metered that it is not influenced by solar gain or human occupancy and is therefore representative and typical of the space temperature. Having access to more than one temperature metering may increase the validity of the approach, especially in large spaces that may be difficult to approximate via just one temperature.
* Daylight events, whereby the event is triggered whenever artificial lighting exceeds a certain threshold, under conditions of adequate daylight.
* Open window events, whereby the event is triggered whenever windows are sensed to be open, resulting in heating/cooling energy loss.
* Solar gain events, whereby the event is triggered when there is a cooling energy loss during summer (e.g., blinds left open) or as a heating energy loss during winter (e.g., blinds left closed).
* Solar-powered DHW events, whereby the event is triggered when hot water is used at a moment when solar heat supply cannot be guaranteed. This may happen because of a number of reasons, such as a low boiler temperature, a user requiring amounts of DHW that cannot be at that moment provided by the solar energy store etc.  
These events have their real-time sensing requirements as well as their calculation details. In addition, they typically will require some calibration, i.e., some process to draw the line between situations that are considered normal and those that are not and deserve attention and user notification. Additionally, it must be stressed that the above events may in some cases be interdependent. Such is, for example, the case of the solar gain and daylight events. However, treating such interdependencies has not been part of the current investigation.  
In this paper, we will restrict the discussion to the first two types of events: off-hours and setpoint deviation, for which field data have been collected and will be reported below.