Building thermal network model and application to temperature regulation

Residential and commercial facilities account for over 40% of energy consumption in the US. Our work shows how fine grain modeling and sensing of this consumption can lead to substantial savings. We construct a thermal network model of a building with the temperatures of the various rooms as states; with the room thermal capacitances and thermal resistances between the rooms as parameters; the external or ambient temperatures as uncontrolled input and heating and cooling loads as control inputs. We show the effects on energy consumption of the random opening and closing of doors or windows. We show that active management of room temperature set points saves up to 20% of energy costs . We also demonstrate the substantial savings-up to 30%-through strategies of keeping certain doors and windows closed.