Pushing the boundaries of traditional heating systems
If a steam-heating engineer from 1912 would timetravel to our present, he would probably find his way around very quickly. Hardly any product system has changed as little during the past century as the heating of buildings. To the manufacturer’s credit, it has to be mentioned that heaters have become much more efficient since the oil shock of the late 1970s – but systemically, most of the buildings have unfortunately remained quite stupid. Why do many air conditioning systems have no clue if there even is someone present in the building? Why do systems – apart from an abstract heating curve – know practically nothing about the building? Why do heating systems consist of nested control loops, although this causes hardly controllable oscillation?
Large amount of energy is wasted senselessly by these limitations.
A system that knows the building
Knowledge about the thermal properties and the behavior of each building opens space for optimizing rules. The initial ideas of alphaEOS’ founders started with these fundamental questions. Today, alphaEOS develops solutions for residential real estate and offices. Their goal is to turn unnecessary loss of energy into comfort for the residents. alphaEOS relies on machine learning: The system gets to know the building and its residents, can anticipate the heat demand and thus make appropriate adjustments.
User and system in dialogue
How does a self-learning system communicate with its users? Of course it would be best if everything always met the needs perfectly. Then alphaEOS would not need any user interface at all. Unfortunately, even intelligent systems can not (yet) read our minds and anticipate every wish.
Therefore, our goal was to give alphaEOS a face and optimal user interfaces. In close dialogue with the development team, experts and users we developed the interaction experience for the first alphaEOS product system.