Lennox Industries Inc.

Janathkumar Manohararaj

Payam Delgoshaei

Sridhar Venkatesh

Eric Berg

Glenn William Kowald

Jeffrey Fontenault

A heating, ventilation, and air conditioning (HVAC) control device is configured to record a plurality of actual occupancy statuses, to determine a plurality of corresponding predicted occupancy statuses, and to compare the plurality of predicted occupancy statuses to the plurality of actual occupancy statuses. The device is further configured to identify conflicting occupancy statuses based on the comparison. A conflicting occupancy status indicates a difference between an actual occupancy status and a corresponding predicted occupancy status. The device is further configured to identify timestamps corresponding with the conflicting occupancy statuses, to identify historical occupancy statuses corresponding with the identified timestamps, and to update the conflicting occupancy statuses in the predicted occupancy schedule with the historical occupancy statuses.

Error correction for predictive schedules for a thermostat

A heating, ventilation, and air conditioning (HVAC) control device configured to receive a user input for controlling an HVAC system, to determine whether the user input indicates an energy saving occupancy setting, and to identify a first plurality of time entries that are associated with a confidence level for a predicted occupancy status that is less than a predetermined threshold value in the predicted occupancy schedule. The device is further configured to modify the predicted occupancy schedule by setting the first plurality of time entries to an away status when the user input indicates an aggressive energy saving occupancy setting. The device is further configured to modify the predicted occupancy schedule by setting the second plurality of time entries to a present status when the user input indicates a conservative energy saving occupancy setting. The device is further configured to output the modified predicted occupancy schedule.

Predictive temperature scheduling for a thermostat using machine learning

A heating, ventilation, and air conditioning (HVAC) control device configured to generate the machine learning model using the first set of weights and the second set of weights. The machine learning model is configured to output a probability that a user is present at the space based on an input that identifies a day of the week and a time of a day. The device is further configured to determine a probability that a user is present at the space for a predicted occupancy schedule using the machine learning model, to determine an occupancy status based on a determined probability that a user is present at the space, and to set a predicted occupancy status in the predicted occupancy schedule based on a determined occupancy status for each time entry. The device is further configured to output the predicted occupancy schedule.

Predictive presence scheduling for a thermostat using machine learning

A heating, ventilation, and air conditioning (HVAC) control device configured to collect event data from the one or more devices and to populate time entries in an occupancy history log with the event data. The event data includes a timestamp indicating a time when an event occurred, a set point temperature value for the HVAC system, and an occupancy status indicating whether a space is occupied. The device is further configured to identify blank time entries in the occupancy history log and to populate the blank time entries by forward filling the occupancy history log using event data from another time entry in the occupancy history log. The device is further configured to output the populated occupancy history log.

Thermostat scheduling using user devices and machine learning

A device configured to operate a Heating, Ventilation, and Air Conditioning (HVAC) system and to receive an audio signal from a microphone while operating HVAC system. The device is further configured to generate a representation of the audio signal, to compare one or more audio signatures to the representation of the audio signal, and to determine that an audio signature from among the one or more audio signatures is not present within the representation of the audio signal. The device is further configured to determine a fault type that is associated with the audio signature that is not present within the representation of the audio signal, to identify a component identifier for a component of the HVAC system that is associated with fault type, and to output the component identifier.

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McKinney, TX, United States of America

Janathkumar Manohararaj