Abstract:
Embodiments provided herein relate to monitoring and reporting household activities. In one embodiment, a method includes: monitoring, via a smart device, one or more activities associated with: a household; analyzing, via the smart device, a processor, or both, at least one characteristic of the one or more activities to discern information about the household; and reporting, via the device, the discerned information.
Abstract:
Embodiments provided herein relate to controlling a household via one or more household policies. In one embodiment, a method includes: receiving, at a processor, a household policy for a household, the household policy related to attaining an end goal; determining, via interpretation of the household policy by the processor, an end goal state of the household policy; incrementally modifying a control trigger threshold of a conditionally controlled smart device over time until the end goal state is reached; wherein the control trigger threshold indicates when the conditionally controlled smart device should be controlled to implement a particular function.
Abstract:
A home energy efficiency platform is described having as its fundamental component a network-connected, multi-sensing learning thermostat that leverages a visually pleasing interactive display associated therewith to encourage energy-saving behavior by a competitive gamesmanship modality, either in terms of self-competition in which a user's energy-saving performance is measured against themselves over time, or in terms of community competition in which a user's energy-saving behavior is measured against a relevant community.
Abstract:
A method includes receiving an estimated time of arrival (ETA) relating to an arrival to an environment, an arrival of an event, arrival of an activity, or a combination thereof; and controlling, configuring, or controlling and configuring a smart device based upon the ETA.
Abstract:
A combined business and technical method is described in which a paid subscription service is offered to provide “premium” HVAC algorithms for a network-connected, multi-sensing learning thermostat. The users who have chosen to pay for the premium subscription service are provided with at least one additional feature, capability, and/or option that is not provided to unpaid “basic” subscribers of a cloud-based thermostat servicing system that is provided for all thermostat owners. According to some embodiments, an on-line interview process is administered to gather additional information for improving the settings of the thermostat. According to some embodiments, an active test is performed to determine thermal characteristics of the structure. According some embodiments, the user guaranteed to at least recoup the cost of the premium service through energy cost savings.
Abstract:
In various embodiments, a hazard detector is presented. The hazard detector may include a hazard detection sensor that detects a presence of a type of hazard. The hazard detector may include a light and a light sensor that senses a brightness level in an ambient environment of the hazard detector. The hazard detector may include a processing system configured to receive an indication of the brightness level in the ambient environment of the hazard detector from the light sensor. The processing system may determine the brightness level in the ambient environment of the hazard detector has reached a threshold value. A status check of one or more components of the hazard detector may be performed. The processing system may cause the light to illuminate using a selected illumination state in response to the determining the brightness level in the ambient environment of the hazard detector has reached the threshold value.
Abstract:
A system includes a thermostat that controls a heating, ventilation, and cooling (HVAC) system of a structure in accordance with a signature-based temperature program. The thermostat includes one or more sensors configured to collect occupant activity data, a network interface configured to communicate with at least one online resource, a memory configured to store a signature-based temperature model, and a processor. The processor is configured to determine a temperature to implement from an output of the signature-based temperature model, wherein a current value of the at least one model input and a current measure of occupant activity are provided as inputs to the signature-based temperature model. The processor is further configured to provide control signals to the HVAC system to implement the determined temperature.
Abstract:
The present disclosure relates to thermostatically controlling a HVAC system according to a temperature program that is at least partially responsive to observed or predicted changes in the type or degree of occupant activity. For example, a thermostat may process collected occupant activity data in conjunction a temperature program to identify a particular temperature setpoint that is associated with a statistically detectable change between a first and a second type or degree of occupant activity. During a time window that includes the identified temperature setpoint, if the thermostat detects the change between the first and the second type or degree of occupant activity in the occupant activity data, the thermostat may responsively implement the temperature associated with the identified temperature setpoint, regardless of whether the current time is prior to, the same as, or subsequent to the time associated with the identified temperature setpoint.
Abstract:
The current application is directed to an intelligent control system that includes intelligent thermostats and remote servers that spread call-home events over time to avoid large peak computational and communications loads on intelligent-control-system servers. The spreading of call-home vents over time is effected by use of call-home splay values pseudorandomly generated for intelligent thermostats.
Abstract:
Embodiments of the invention describe thermostats that use model predictive controls and related methods. A method of controlling a thermostat using a model predictive control may involve determining a parameterized model. The parameterized model may be used to predicted ambient temperature values for an enclosure. A set of radiant heating system control strategies may be selected for evaluation to determine an optimal control strategy from the set of control strategies. To determine the optimal control strategy, a predictive algorithm may be executed, in which each control strategy is applied to the parameterized model to predict an ambient temperature trajectory and each ambient temperature trajectory is processed in view of a predetermined assessment function. Processing the ambient temperature trajectory in this manner may involve minimizing a cost value associated with the ambient temperature trajectory. The radiant heating system may subsequently be controlled according to the selected optimal control strategy.