Abstract:
In a multi-sensing, wirelessly communicating learning thermostat that uses power-harvesting to charge an internal battery, methods are disclosed for ensuring that the battery does not become depleted or damaged while at the same time ensuring selected levels of thermostat functionality. Battery charge status is monitored to determine whether the present rate of power usage needs to be stemmed. If the present rate of power usage needs to be stemmed, then a progression of performance levels and/or functionalities are scaled back according to a predetermined progressive power conservation algorithm. In a less preferred embodiment, there is a simple progressive shutdown of functionalities turned off in sequence until the desired amount of discharge stemming is reached. Battery charge preservation measures are also described for cases when an interruption of external supply power used to recharge the battery is detected.
Abstract:
The current application is directed to an intelligent-thermostat-controlled environmental-conditioning system in which computational tasks and subcomponents with associated intelligent-thermostat functionalities are distributed to one or more of concealed and visible portions of one or more intelligent thermostats and, in certain implementations, to one or more intermediate boxes. The intelligent thermostats are interconnected to intermediate boxes by wired and/or wireless interfaces and intelligent thermostats intercommunicate with one another by wireless communications. Wireless communications include communications through a local router and an ISP, 3G and 4G wireless communications through a mobile service provider. Components of the intelligent- thermostat-controlled environmental-conditioning system may also be connected by wireless communications to remote computing facilities.