Abstract: Apparatus, systems, methods, and related computer program products for optimizing a schedule of setpoint temperatures used in the control of an HVAC system. The systems disclosed include an energy management system in operation with an intelligent, network-connected thermostat located at a structure. The thermostat includes a schedule of setpoint temperatures that is used to control an HVAC system associated with a structure in which the thermostat is located. The schedule of setpoint temperatures is continually adjusted by small, unnoticeable amounts so that the schedule migrates from the original schedule to an optimal schedule. The optimal schedule may be optimal in terms of energy consumption or some other terms.

Abstract: Apparatus, systems, methods, and related computer program products for optimizing a schedule of setpoint temperatures used in the control of an HVAC system. The systems disclosed include an energy management system in operation with an intelligent, network-connected thermostat located at a structure. The thermostat includes a schedule of setpoint temperatures that is used to control an HVAC system associated with a structure in which the thermostat is located. The schedule of setpoint temperatures is continually adjusted by small, unnoticeable amounts so that the schedule migrates from the original schedule to an optimal schedule. The optimal schedule may be optimal in terms of energy consumption or some other terms.

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: 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: Techniques for determining and using a thermodynamic model that characterizes a thermodynamic response of an enclosure conditioned by an HVAC system are disclosed. To determine a thermodynamic model, temperature information when the HVAC system operates in a first state may first be received. A response interval may then be determined where the response interval indicates an estimated time between when the HVAC system begins operating in the first state and when the temperature within the enclosure begins to change in a direction associated with the first state. Weighting factors corresponding to basis functions may then be determined, where the weighted basis functions characterize the temperature trajectory of the enclosure in response to the HVAC system operating in the first 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.

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.

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: Apparatus, systems, methods, and related computer program products for optimizing a schedule of setpoint temperatures used in the control of an HVAC system. The systems disclosed include an energy management system in operation with an intelligent, network-connected thermostat located at a structure. The thermostat includes a schedule of setpoint temperatures that is used to control an HVAC system associated with a structure in which the thermostat is located. The schedule of setpoint temperatures is continually adjusted by small, unnoticeable amounts so that the schedule migrates from the original schedule to an optimal schedule. The optimal schedule may be optimal in terms of energy consumption or some other terms.

Abstract: The current application is directed to intelligent controllers that use sensor output and electronically stored information 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/or modify control schedules with respect to the presence and absence of the one or more entities. The intelligent controllers selectively carry out scheduled control operations during periods of time when one or more types of entities are determined not to be in a controlled environment.

Abstract: Apparatus, systems, methods, and related computer program products for generating and implementing thermodynamic models of a structure. Thermostats disclosed herein are operable to control an HVAC system. In controlling the HVAC system, a need to determine an expected indoor temperature profile for a particular schedule of setpoint temperatures may arise. To make such a determination, a thermodynamic model of the structure may be used. The thermodynamic model may be generated by fitting weighting factors of a set of basis functions to a variety of historical data including time information, temperature information, and HVAC actuation state information. The set of basis functions characterize an indoor temperature trajectory of the structure in response to a change in HVAC actuation state, and include an inertial carryover component that characterizes a carryover of a rate of indoor temperature change that was occurring immediately prior to the change in actuation state.

Abstract: Apparatus, systems, methods, and related computer program products for carrying out a demand response (DR) event via an intelligent, network-connected thermostat associated with a structure. The systems disclosed include an energy management system in operation with an intelligent, network-connected thermostat located at a structure. The thermostat is operable to control an HVAC system. Control during a DR event period may be performed based on an optimal control trajectory of the HVAC system, where the control trajectory is optimal in that it minimizes a cost function comprising a combination of a first factor representative of a total energy consumption during the DR event period, a second factor representative of a metric of occupant discomfort, and a third factor representative of deviations of a rate of energy consumption over the DR event period.

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.

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 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: 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.

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.