Abstract:
Systems and methods are described for interactively, graphically displaying and reporting performance information to a user of an HVAC system controlled by a self-programming network-connected thermostat. The information is made on a remote display device such as a smartphone, tablet computer or other computer, and includes a graphical daily or monthly summary each of several days or months respectively. In response to a user selection of a day, detailed performance information is graphically displayed that can include an indication of HVAC activity on a timeline, the number of hours of HVAC activity, as well as one or more symbols on a timeline indicating setpoint changes, and when a setpoint was changed due to non-occupancy.
Abstract:
The current application is directed to intelligent controllers that continuously, periodically, or intermittently monitor progress towards one or more control goals under one or more constraints in order to achieve control that satisfies potentially conflicting goals. An intelligent controller may alter aspects of control, dynamically, while the control is being carried out, in order to ensure that goals are obtained and a balance is achieved between potentially conflicting goals. The intelligent controller uses various types of information to determine an initial control strategy as well as to dynamically adjust the control strategy as the control is being carried out.
Abstract:
The current application is directed to intelligent controllers that initially aggressively learn, and then continue, in a steady-state mode, to monitor, learn, and modify one or more control schedules that specify a desired operational behavior of a device, machine, system, or organization controlled by the intelligent controller. An intelligent controller generally acquires one or more initial control schedules through schedule-creation and schedule-modification interfaces or by accessing a default control schedule stored locally or remotely in a memory or mass-storage device. The intelligent controller then proceeds to learn, over time, a desired operational behavior for the device, machine, system, or organization controlled by the intelligent controller based on immediate-control inputs, schedule-modification inputs, and previous and current control schedules, encoding the desired operational behavior in one or more control schedules and/or sub-schedules.
Abstract:
An electronic device associated with a lock device obtains a number of users detected within a premises, and detects a trigger event related to a lock device and premises. When the trigger event is detected, a target state of the lock device is determined based on: (1) the number of users within the premises, (2) user security profiles indicating a desired target state of the lock device when a respective user is within the premises, (3) locations of detected users; (4) user states of detected users indicating whether the respective user is asleep or active; and/or (5) a current premises mode, including an armed state and a disarmed state. A current state of the lock device is determined, and if the current state and the target state of the lock device are not the same, instructions are provided to the lock device based on the target state.
Abstract:
The current application is directed to intelligent controllers that continuously, periodically, or intermittently monitor progress towards one or more control goals under one or more constraints in order to achieve control that satisfies potentially conflicting goals. An intelligent controller may alter aspects of control, dynamically, while the control is being carried out, in order to ensure that goals are obtained and a balance is achieved between potentially conflicting goals. The intelligent controller uses various types of information to determine an initial control strategy as well as to dynamically adjust the control strategy as the control is being carried out.
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.
Abstract:
An electronic device associated with a lock device obtains a number of users detected within a premises, and detects a trigger event related to a lock device and premises. When the trigger event is detected, a target state of the lock device is determined based on: (1) the number of users within the premises, and (2) user security profiles indicating a desired target state of the lock device when a respective user is within the premises. A current state of the lock device is determined, and if the current state and the target state of the lock device are not the same, instructions are provided to the lock device based on the target state.
Abstract:
The current application is directed to intelligent controllers that use sensor output and electronically stored information, including one or more of electronically stored rules, parameters, and instructions, to determine whether or not one or more types of entities are present within an area, volume, or environment monitored by the intelligent controllers. The intelligent controllers select operational modes and modify control schedules with respect to the presence and absence of the one or more entities. The intelligent controllers employ feedback information to continuously adjust the electronically stored parameters and rules in order to minimize the number of incorrect inferences with respect to the presence or absence of the one or more entities and in order to maximize the efficiency by which various types of systems controlled by the intelligent controllers carry out selected operational modes.
Abstract:
The current application is directed to intelligent controllers that continuously, periodically, or intermittently monitor progress towards one or more control goals under one or more constraints in order to achieve control that satisfies potentially conflicting goals. An intelligent controller may alter aspects of control, dynamically, while the control is being carried out, in order to ensure that goals are obtained and a balance is achieved between potentially conflicting goals. The intelligent controller uses various types of information to determine an initial control strategy as well as to dynamically adjust the control strategy as the control is being carried out.