Trade-off between Energy Consumption and Comfort Experience in Smart Buildings
The thermal comfort experience in conditioned environments is closely related to the indoor temperature andvaries mainly due to the dynamics of occupant state and the environmental state. The heating or cooling required to achievethe desired temperature and comfort influences the energy consumption. This article presents a multi-agent control systemthat primarily regulates thermal comfort rather than the indoor temperature. We developed this comfort regulator that basedon (i) the difference between the desired level of comfort and the current level of comfort and (ii) the difference between thecurrent temperature and the set point temperature adapts the set point temperature in order to achieve the desired comfort.An occupancy prediction algorithm and expert rules were designed to efficiently reduce unnecessary energy consumptionduring periods when the home is not occupied and the comfort experience is therefore not important. The results of experimentsare presented in a comfort/energy-consumption space. The comfort/energy-consumption space shows how the finalresult is influenced by (i) different versions of learning algorithms and (ii) different comfort threshold values. Comparingthe comfort/energy-consumption spaces for different occupancy patterns shows that the rule settings have similar impact oncontrol performance, which indicates that the rules are general. In nearly all experiments, the proposed multi-agent controlsystem assured better comfort experience with small increase of energy consumption compared to reactive control system.