Anticipatory system for T--H--C dynamics in room with real and virtual sensors

With modern technology and advanced models, it is possible to rather accurately anticipate the changes of weather parameters, such as temperature or precipitation, for a couple of days in advance. On the other hand, predicting dynamics of internal parameters, such as office spaces, can be tricky, as there are many variables that influence them, and we do not have information on their status. Being able to predict how parameters are changing would allow us to recommend appropriate actions to improve their work/living environment. In this paper, we present an anticipatory system, that is built from virtual sensors, which estimates number of occupants in the room and detects the state of the windows. Together with data from real sensors ontology, as coded knowledge, outputs reasonable actions that may improve internal environment. We built models, which anticipate dynamics of internal parameters temperature (T), humidity (H) and CO2 concentration (C), regarding all combinations of actions.