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: A thermostat includes a processing system configured to control an HVAC system. The thermostat may also include a plurality of HVAC connectors configured to receive corresponding HVAC control wires, and a connection sensing module configured to determine the identities of HVAC connectors into which corresponding wires have been inserted. The processing system may be further configured to identify, based on the subset of HVAC connectors, whether (i) only a single possible HVAC system configuration is indicated thereby, or (ii) multiple possible HVAC system configurations are indicated thereby, resolve a particular one of the multiple possible HVAC system configurations that is applicable, and operate the HVAC system according to the HVAC system configuration.

Abstract: A thermostat may include a memory and a processing system. The processing system may operate by determining a set of wake-up conditions for the processor to enter into a second operating state from a first operating state, the set of wake-up conditions including at least one threshold value associated with at least one environmental condition; causing the set of wake-up conditions to be stored in the memory; operating in a first mode in which the processor is in the first operating state during a time interval subsequent to causing the set of wake-up conditions to be stored in the memory; determining, while the processor is in the first operating state, whether at least one of the set of wake-up conditions has been met; and then operating in a second mode in which the processor is in the second operating state.

Abstract: The current application is directed to intelligent controllers that continuously, periodically, or intermittently calculate and display the time remaining until a control task is projected to be completed by the intelligent controller. In general, the intelligent controller employs multiple different models for the time behavior of one or more parameters or characteristics within a region or volume affected by one or more devices, systems, or other entities controlled by the intelligent controller. The intelligent controller collects data, over time, from which the models are constructed and uses the models to predict the time remaining until one or more characteristics or parameters of the region or volume reaches one or more specified values as a result of intelligent controller control of one or more devices, systems, or other entities.

Abstract: Systems and methods are provided for efficiently controlling energy-consuming systems, such as heating, ventilation, or air conditioning (HVAC) systems. For example, an electronic device used to control an HVAC system may encourage a user to select energy-efficient temperature setpoints. Based on the selected temperature setpoints, the electronic device may generate or modify a schedule of temperature setpoints to control the HVAC system.

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 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 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: The current application is directed to intelligent controllers that continuously, periodically, or intermittently calculate and display the time remaining until a control task is projected to be completed by the intelligent controller. In general, the intelligent controller employs multiple different models for the time behavior of one or more parameters or characteristics within a region or volume affected by one or more devices, systems, or other entities controlled by the intelligent controller. The intelligent controller collects data, over time, from which the models are constructed and uses the models to predict the time remaining until one or more characteristics or parameters of the region or volume reaches one or more specified values as a result of intelligent controller control of one or more devices, systems, or other entities.

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: A thermostat includes a processing system configured to control an HVAC system. The thermostat may also include a plurality of HVAC connectors configured to receive corresponding HVAC control wires, and a connection sensing module configured to determine the identities of HVAC connectors into which corresponding wires have been inserted. The processing system may be further configured to identify, based on the subset of HVAC connectors, whether (i) only a single possible HVAC system configuration is indicated thereby, or (ii) multiple possible HVAC system configurations are indicated thereby, resolve a particular one of the multiple possible HVAC system configurations that is applicable, and operate the HVAC system according to the HVAC system configuration.

Abstract: The current application is directed to intelligent controllers that continuously, periodically, or intermittently calculate and display the time remaining until a control task is projected to be completed by the intelligent controller. In general, the intelligent controller employs multiple different models for the time behavior of one or more parameters or characteristics within a region or volume affected by one or more devices, systems, or other entities controlled by the intelligent controller. The intelligent controller collects data, over time, from which the models are constructed and uses the models to predict the time remaining until one or more characteristics or parameters of the region or volume reaches one or more specified values as a result of intelligent controller control of one or more devices, systems, or other entities.

Abstract: HVAC schedules may be programmed for a thermostat using a combination of pre-existing schedules or templates and automated schedule learning. For example, a pre-existing schedule may be initiated on the thermostat and the automated schedule learning may be used to update the pre-existing schedule based on users' interactions with the thermostat. The preexisting HVAC schedules may be stored on a device or received from a social networking service or another online service that includes shared HVAC schedules.

Abstract: Embodiments of the invention describe thermostats that are configured to precondition an enclosure and methods for performing the same. According to one embodiment, a method of preconditioning an enclosure includes providing a thermostat and computing a set of preconditioning criteria information (PCI) with said thermostat. The computed PCI is typically representative of time and ambient temperature conditions for which preconditioning should be performed. The PCI may be stored in memory and used to compare against a current time and current ambient temperature condition of the enclosure to determine whether to enter the thermostat into a preconditioning state. If a determination is made that the PCI criteria are satisfied, the thermostat may be entered into the preconditioning state to heat or cool the enclosure. One or more of these processes may be performed while a processor of the thermostat is in a relatively high power mode or relatively low power mode.

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 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: A thermostat may include a memory and a processing system. The processing system may operate by determining a set of wake-up conditions for the processor to enter into a second operating state from a first operating state, the set of wake-up conditions including at least one threshold value associated with at least one environmental condition; causing the set of wake-up conditions to be stored in the memory; operating in a first mode in which the processor is in the first operating state during a time interval subsequent to causing the set of wake-up conditions to be stored in the memory; determining, while the processor is in the first operating state, whether at least one of the set of wake-up conditions has been met; and then operating in a second mode in which the processor is in the second operating state.

Abstract: Systems and methods are described for controlling fan-only cooling duration following normal air conditioning operation. Following normal AC cooling, economical fan cooling is used. The duration of the fan cooling period is adjusted based on temperature measurements made during the previous cooling cycle that ended with fan cooling. An expected temperature drop to be provided by fan cooling as well as an expected time to achieve that drop is calculated based on prior measurements. The expected values are then used improve fan cooling for subsequent cooling cycles. In some cases, fan cooling is not initiated unless: (1) a time limit has an elapsed, such that sufficient condensation is allowed to form on the evaporator coil during the first phase, and (2) indoor relative humidity is below a predetermined threshold.

Abstract: A thermostat includes a housing, a user interface, and a processing system configured to control an HVAC system using setpoint temperature values. The thermostat may also include a plurality of HVAC connectors configured to receive corresponding HVAC control wires, and a connection sensing module configured to determine the identities of HVAC connectors into which corresponding wires have been inserted. The processing system may be further configured to identify, based on the subset of HVAC connectors, whether (i) only a single possible HVAC system configuration is indicated thereby, or (ii) multiple possible HVAC system configurations are indicated thereby, resolve a particular one of the multiple possible HVAC system configurations that is applicable based on a user response to an inquiry presented on the user interface, and operate the HVAC system according to the HVAC system configuration.

Abstract: A thermostat includes at least a housing, a user interface, a memory, an environmental sensor, and a processing system. The processing system may be configured to operate in a wake state and a sleep state by determining wake-up conditions upon which the processor is to enter into the wake state from the sleep state that includes a threshold value associated with an environmental condition sensed by the environmental sensor, causing the wake-up conditions to be stored in the memory, operating in the sleep state during a time interval subsequent to causing the wake-up conditions to be stored in the memory, determining whether at least one of the wake-up conditions has been met, and operating in the wake state upon a determination that the at least one wake-up condition has been met.