Abstract: Methods for controlling temperature in a conditioned enclosure such as a dwelling are described that include an “auto-away” and/or “auto-arrival” feature for detecting unexpected absences which provide opportunities for significant energy savings through automatic adjustment of the setpoint temperature. According to some preferred embodiments, when no occupancy has been detected for a minimum time interval, an “auto-away” feature triggers a changes of the state of the enclosure, and the actual operating setpoint temperature is changed to a predetermined energy-saving away-state temperature, regardless of the setpoint temperature indicated by the normal thermostat schedule. The purpose of the “auto away” feature is to avoid unnecessary heating or cooling when there are no occupants present to actually experience or enjoy the comfort settings of the schedule, thereby saving energy.

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: Methods for controlling temperature in a conditioned enclosure such as a dwelling are described that include an “auto-away” and/or “auto-arrival” feature for detecting unexpected absences which provide opportunities for significant energy savings through automatic adjustment of the setpoint temperature. According to some preferred embodiments, when no occupancy has been detected for a minimum time interval, an “auto-away” feature triggers a changes of the state of the enclosure, and the actual operating setpoint temperature is changed to a predetermined energy-saving away-state temperature, regardless of the setpoint temperature indicated by the normal thermostat schedule. The purpose of the “auto away” feature is to avoid unnecessary heating or cooling when there are no occupants present to actually experience or enjoy the comfort settings of the schedule, thereby saving energy.

Abstract: Systems and methods are described for predicting and/or detecting occupancy of an enclosure, such as a dwelling or other building, which can be used for a number of applications. An a priori stochastic model of occupancy patterns based on information of the enclosure and/or the expected occupants of the enclosure is used to pre-seed an occupancy prediction engine. Along with data from an occupancy sensor, the occupancy prediction engine predicts future occupancy of the enclosure. Various systems and methods for detecting occupancy of an enclosure, such as a dwelling, are also described.

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: A thermostat for controlling an HVAC system and related systems, methods, and computer program products for facilitating user-friendly installation of the thermostat are described. For one embodiment, automated installation verification is performed by the thermostat by automatically sensing which wires have been inserted, selecting a candidate HVAC operating function (e.g., heating or cooling) that is consistent with a subset of HVAC signal types indicated by the inserted wires, applying control signals to the HVAC system to invoke that HVAC operating function, and processing a time sequence of acquired temperature readings to determine whether that HVAC operating function was successfully carried out. For one embodiment, the initial automated testing of the heating and cooling functions are only carried out at times for which such heating or cooling function would normally be invoked during normal operation of the thermostat. Automated determination of a heat pump call convention is also described.

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: Modeling the behavior of an enclosure for use by a control system of an HVAC system is described. A model for the enclosure that describes the enclosure's behavior for use by the control system is updated based on weather forecast data. The weather forecast data can include predictions more than 24 hours in the future, and can include predictions on temperature, humidity and/or dew point, solar output, precipitation. The model for the enclosure can also be updated based on additional information and data. The model for the enclosure can be updated based also on an enclosure model stored in a database, and/or enclosure information from a user. The model can be updated based on active testing of the enclosure which can be performed automatically or in response to user input. The testing can include heating and/or cooling the enclosure at times when the enclosure is not likely occupied.

Abstract: Systems and methods are described for predicting and/or detecting occupancy of an enclosure, such as a dwelling or other building, which can be used for a number of applications. An a priori stochastic model of occupancy patterns based on information of the enclosure and/or the expected occupants of the enclosure is used to pre-seed an occupancy prediction engine. Along with data from an occupancy sensor, the occupancy prediction engine predicts future occupancy of the enclosure. Various systems and methods for detecting occupancy of an enclosure, such as a dwelling, are also described.

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: A thermostat for controlling an HVAC system and related systems, methods, and computer program products for facilitating user-friendly installation of the thermostat are described. For one embodiment, automated installation verification is performed by the thermostat by automatically sensing which wires have been inserted, selecting a candidate HVAC operating function (e.g., heating or cooling) that is consistent with a subset of HVAC signal types indicated by the inserted wires, applying control signals to the HVAC system to invoke that HVAC operating function, and processing a time sequence of acquired temperature readings to determine whether that HVAC operating function was successfully carried out. For one embodiment, the initial automated testing of the heating and cooling functions are only carried out at times for which such heating or cooling function would normally be invoked during normal operation of the thermostat. Automated determination of a heat pump call convention is also described.

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 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: The current application is related to environmental-conditioning systems controlled by intelligent controllers and, in particular, to an intelligent-thermostat-controlled HVAC system that detects and ameliorates control coupling between intelligent thermostats. Control coupling can lead to inefficient HVAC operation. When control coupling is detected, a settings-adjustment directive is sent to at least one intelligent thermostat to adjust one or more intelligent-thermostat settings, including an HVAC-cycle-initiation delay parameter, swing parameter, and a parameter that indicates whether or not an intelligent thermostat should first obtain confirmation or permission before initiating an HVAC cycle.

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: Systems and methods for modeling the behavior of an enclosure for use by a control system of an HVAC system are described. A model for the enclosure that describes the behavior of the enclosure for use by the control system is updated based on a weather forecast data. The weather forecast data can include predictions more than 24 hours in the future, and can include predictions such as temperature, humidity and/or dew point, solar output, precipitation. The model for the enclosure can also be updated based on additional information and data such as historical weather data such as temperature, humidity, wind, solar output and precipitation, occupancy data, such as predicted and/or detected occupancy data, calendar data, and data from the one or more weather condition sensors that sense current parameters such as temperature, humidity, wind, precipitation, and/or solar output.

Abstract: Systems and methods are described for predicting and/or detecting occupancy of an enclosure, such as a dwelling or other building, which can be used for a number of applications. An a priori stochastic model of occupancy patterns based on information of the enclosure and/or the expected occupants of the enclosure is used to pre-seed an occupancy prediction engine. Along with data from an occupancy sensor, the occupancy prediction engine predicts future occupancy of the enclosure. Various systems and methods for detecting occupancy of an enclosure, such as a dwelling, are also described.