Heating
We used the data from temperature sensors to find unusual patterns patterns that pointed to inefficiencies of the heating systems of the building. To find these patterns, we used unsupervised machine learning techniques in order to cluster daily temperature curves into distinct categories and spot abnormal behaviour. Below you can see three clusters of daily averaged temperature curves from rooms that we were able to identify. These rooms were overheating during the day and night and indicate a suboptimal usage of the building’s HVAC system leading to wasted energy. Optimising the heating behaviour in those rooms would lead to up to 18% reductions in energy consumption.

Inspecting the causes of these abnormal temperature patterns, we were able to identify a few rooms in which the radiators were actually running at night during the summer time. The cooling system had to compensate for this during the day, increasing the energy waste even more.
Finally, by applying anomaly detection to the temperature curves, we identified that a control unit of the cooling system was broken.
Room occupancy vs cooling
Motion and lightning sensors give insight into when a room is occupied and how it is used. By analysing room occupancy and comparing this data to the ventilation system of the building we were able to detect periods of time in which rooms were being cooled down at full intensity over the period of several weeks without a person ever being present in the room.
By optimising this behaviour, CO2 emission can be drastically lowered, and savings potentials in energy consumption for ventilation systems can be as high as 30%.
Room occupancy vs lighting
During a working day, it happens that people will be too forgetful, stressed, or distracted to turn the lights of when leaving a room. And so it happens that lights are left on in rooms even though everyone has left. By combining data from motion and luminosity sensors we were able to automatically detect patterns in which lights were left on in unoccupied rooms, especially during the night.
In fact, up to 75% of energy consumption for lighting accounts for times in which lighting is not even needed. Again, these are non-negligible saving potentials, especially for large commercial buildings.