Predictive presence scheduling for a thermostat using machine learning

Patent metadata

Field	Value
Publication number	US-11255561-B2
Application number	US-201916731974-A
Country	US
Kind code	B2
Filing date	Dec 31, 2019
Priority date	Dec 31, 2019
Publication date	Feb 22, 2022
Grant date	Feb 22, 2022

Abstract

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.

First claim

The invention claimed is:

1. A heating, ventilation, and air conditioning (HVAC) control device, comprising:
a memory operable to store:
an occupancy history log that comprises:
a plurality of occupancy statuses over a predetermined time period, wherein each occupancy status indicates one of a present status and an away status for a user within a space; and
a plurality of timestamps, wherein each timestamp is associated with an occupancy status from among the plurality of occupancy statuses; and

a predicted occupancy schedule comprising a plurality of time entries, wherein:
each time entry corresponds with a day of a week and a time of a day; and
each time entry is associated with a predicted occupancy status; and

a processor operably coupled to the memory, configured to:
determine a first set of weights for a machine learning model based on the occupancy history log, wherein each of the first plurality of weights is associated with a day of the week;
determine a second set of weights for the machine learning model based on the occupancy history log, wherein each of the second plurality of weights is associated with a time of a day;
generate a machine learning model using the first set of weights and the second set of weights, wherein:
the machine learning model is configured to output a probability that the user is present at the space based on an input that identifies a day of the week and a time of a day; and
generating the machine learning model comprises:
associating the first set of weights with a first set of variables that correspond with days of the week; and
associating the second set of weights with a second set of variables that correspond with times of a day;

determine a probability that the user is present at the space for each time entry in the predicted occupancy schedule using the machine learning model, wherein determining the probability that the user is present at the space comprises:
selecting a first time entry from the predicted occupancy schedule;
identifying a day and a time that is associated with the first time entry;
setting a first variable in the machine learning model that corresponds with the identified day to a value of one, wherein setting the first variable to a value of one identifies a first weight value;
setting a second variable in the machine learning model that corresponds with the identified time to a value of one, wherein setting the second variable to a value of one identifies a second weight value;
setting variables in the machine learning model that do not correspond with the identified day and the identified time to a value of zero, wherein setting the variables that do not correspond with the identified day and the identified time to a value of zero removes corresponding weights from the machine learning model; and
computing the probability based on the first weight value and the second weight value;

determine an occupancy status for each time entry based on a determined probability that the user is present at the space corresponding with each time entry, wherein determining the occupancy status comprises:
selecting a second time entry;
comparing the determined probability that is associated with the second time entry to a threshold value; and
determining the occupancy status is a present status when the determined probability is greater than or equal to the threshold value;

set a predicted occupancy status for each time entry based on a determined occupancy status for each time entry; and
output the predicted occupancy schedule.

2. The device of claim 1 , wherein the processor is further configured to:
determine a number of occupancy statuses in the occupancy history log;
compare the number of occupancy statuses in the occupancy history log to a predetermined threshold value, wherein the predetermined threshold value corresponds with a minimum number of occupancy statuses for generating a machine learning model; and
determine that the number of occupancy statuses in the occupancy history log is greater than the predetermined threshold value before determining the first set of weights for the machine learning model.

3. The device of claim 1 , wherein:
the occupancy history log associates occupancy statuses with minutes of a day; and
the processor is further configured to convert the occupancy history log to associate occupancy statutes with hours of a day before determining the first set of weights for the machine learning model.

4. The device of claim 1 , wherein:
the processor is further configured to:
obtain historical weather information; and
determine a third set of weights for the machine learning model based on the historical weather information, wherein each of the third plurality of weights is associated with a temperature at a time of a day; and

the input for the machine learning model further identifies a forecasted temperature corresponding with a day of the week and a time of a day.

5. The device of claim 1 , wherein outputting the predicted occupancy schedule comprises presenting the predicted occupancy schedule on a graphical user interface.

6. The device of claim 1 , wherein outputting the predicted occupancy schedule comprises sending the predicted occupancy schedule to a thermostat for an HVAC system.

7. The device of claim 1 , wherein outputting the predicted occupancy schedule comprises storing the predicted occupancy schedule in a memory.

8. A predictive presence scheduling method, comprising:
determining a first set of weights for a machine learning model based on an occupancy history log, wherein:
each of the first plurality of weights is associated with a day of the week; and
the occupancy history log that comprises:
a plurality of occupancy statuses over a predetermined time period, wherein each occupancy status indicates one of a present status and an away status for a user within a space; and
a plurality of timestamps, wherein each timestamp is associated with an occupancy status from among the plurality of occupancy statuses;

determining a second set of weights for the machine learning model based on the occupancy history log, wherein each of the second plurality of weights is associated with a time of a day;
generating a machine learning model using the first set of weights and the second set of weights, wherein:
the machine learning model is configured to output a probability that the user is present at the space based on an input that identifies a day of the week and a time of a day; and
generating the machine learning model comprises:
associating the first set of weights with a first set of variables that correspond with days of the week; and
associating the second set of weights with a second set of variables that correspond with times of a day;

determining a probability that the user is present at the space for each time entry in a predicted occupancy schedule using the machine learning model, wherein:
the predicted occupancy schedule comprises a plurality of time entries, wherein:
each time entry corresponds with a day of a week and a time of a day; and
each time entry is associated with a predicted occupancy status; and

determining the probability that the user is present at the space comprises:
selecting a first time entry from the predicted occupancy schedule;
identifying a day and a time that is associated with the first time entry;
setting a first variable in the machine learning model that corresponds with the identified day to a value of one, wherein setting the first variable to a value of one identifies a first weight value;
setting a second variable in the machine learning model that corresponds with the identified time to a value of one, wherein setting the second variable

Assignees

• Lennox Ind Inc

Inventors

• Venkatesh Sridhar
• Delgoshaei Payam
• Manohararaj Janathkumar

Classifications

• G05B2219/2614

  HVAC, heating, ventillation, climate control · CPC title

• F24F2130/10

  Weather information or forecasts · CPC title

• G05B13/026

  using a predictor · CPC title

• F24F2110/10

  Temperature · CPC title

• F24F11/64

  using pre-stored data · CPC title