Methods and apparatus for parameter based learning and adjusting temperature preferences

• US 10,386,795 B2
• Filed: 10/30/2014
• Issued: 08/20/2019
• Est. Priority Date: 10/30/2014
• Status: Active Grant

• Current Assignee: Vivint Incorporated (NRG Energy, Inc.)
• Original Assignee: Vivint Incorporated (NRG Energy, Inc.)
• Inventors: Vega, JonPaul
• Primary Examiner(s): Afshar, Kamran
• Assistant Examiner(s): Buss, Benjamin J
• Application Number:
  US14/528,775
• Publication Number:
  US 20160123617A1
• Time in Patent Office: 1,755 Days
• Field of Search: None
• CPC Class Codes: F24F 11/30 for purposes related to the operation of the system, e.g. for safety or monitoring
• CPC Class Codes: F24F 11/46 Improving electric energy efficiency or saving
• CPC Class Codes: F24F 11/47 Responding to energy costs
• CPC Class Codes: F24F 11/52 Indication arrangements, e.g. displays
• CPC Class Codes: F24F 11/526 giving audible indications
• CPC Class Codes: F24F 11/56 Remote control
• CPC Class Codes: F24F 11/58 using Internet communication
• CPC Class Codes: F24F 11/62 characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
• CPC Class Codes: F24F 11/64 using pre-stored data
• CPC Class Codes: F24F 11/65 for selecting an operating mode
• CPC Class Codes: F24F 2110/10 Temperature
• CPC Class Codes: F24F 2110/12 of the outside air
• CPC Class Codes: F24F 2110/20 Humidity
• CPC Class Codes: F24F 2110/22 of the outside air
• CPC Class Codes: F24F 2120/10 Occupancy
• CPC Class Codes: F24F 2120/12 Position of occupants
• CPC Class Codes: F24F 2120/20 Feedback from users
• CPC Class Codes: F24F 2130/00 Control inputs relating to environmental factors not covered by group F24F2110/00
• CPC Class Codes: F24F 2130/10 Weather information or forecasts
• CPC Class Codes: G05B 13/0265 the criterion being a learning criterion
• CPC Class Codes: G05B 2219/2614 : HVAC, heating, ventillation, climate control
• CPC Class Codes: G06N 20/00 : Machine learning

Abstract

A method for relative temperature preference learning is described. In one embodiment, the method includes identifying one or more current settings of a thermostat located at a premises, identifying one or more current indoor and outdoor conditions, calculating a current indoor differential between the current indoor temperature and the current target temperature, calculating a current outdoor differential between the current outdoor temperature and the current target temperature, and learning temperature preferences based on an analysis of the one or more current indoor conditions and the one or more current outdoor conditions. The one or more current settings of the thermostat include at least one of a current target temperature, current runtime settings, and current airflow settings. The one or more current indoor and outdoor conditions include at least one of a current temperature, current humidity, current indoor airflow, current atmospheric pressure, current level of precipitation, and current cloud cover.

Claims

1. A method for relative temperature preference learning, comprising:

identifying one or more current settings of a thermostat, wherein the thermostat is a component of a heating ventilation air conditioning (HVAC) system located at a premises, wherein the one or more current settings of the thermostat include at least one of a current target temperature, current runtime settings, and current airflow settings of the HVAC system;

identifying one or more current indoor conditions, wherein the one or more current indoor conditions include at least one of a current indoor temperature, current indoor humidity, and current indoor airflow;

identifying one or more current outdoor conditions, wherein the one or more current outdoor conditions include at least one of a current outdoor temperature, a current outdoor humidity, current atmospheric pressure, current level of precipitation, and current level of cloud cover;

calculating a current indoor differential between the current indoor temperature and the current target temperature;

calculating a current outdoor differential between the current outdoor temperature and the current target temperature;

identifying historical temperature data stored in a database, the historical temperature data comprising a plurality of historical target temperatures, wherein each record of the plurality of historical target temperatures is associated with an historical indoor temperature and an historical outdoor temperature;

calculating an average indoor differential, based at least in part on a set of calculated differentials between the identified historical target temperatures and associated historical indoor temperatures;

calculating an average outdoor differential, based at least in part on a set of calculated differentials between the identified historical target temperatures and associated historical outdoor temperatures;

communicating, over a time period, suggested target temperatures to an occupant of the premises;

tracking, over the time period, user acceptance of the suggested target temperatures;

learning temperature preferences for the target temperature based on an analysis of the one or more current indoor conditions, the one or more current outdoor conditions, the calculated current outdoor differential, the calculated indoor differential, the calculated average outdoor differential, the calculated average indoor differential, the tracked user acceptance, and the stored historical temperature data; and

adjusting, by a temperature preference module, the current target temperature of the HVAC system based at least in part on the learned temperature preferences for the target temperature.

2. The method of claim 1, comprising:

upon determining the user acceptance of the suggested target temperatures over the time period satisfies a user acceptance threshold, automatically adjusting the current target temperature without communicating the suggested target temperatures for user acceptance.

3. The method of claim 1, comprising:

determining whether the current indoor differential satisfies an indoor differential threshold, the indoor differential threshold being based on the calculated average indoor differential.

4. The method of claim 3, comprising:

upon determining the current indoor differential satisfies the indoor differential threshold and the premises is occupied, calculating a suggested target temperature based on the learned temperature preferences, the learned temperature preferences being based on the analysis of the one or more current indoor conditions and the one or more current outdoor conditions in relation to the stored historical temperature data.

5. The method of claim 1, comprising:

determining whether the current outdoor differential satisfies an outdoor differential threshold, the outdoor differential threshold being based on the calculated average outdoor differential.

6. The method of claim 5, comprising:

upon determining the current outdoor differential satisfies the outdoor differential threshold and the premises is occupied, calculating a suggested target temperature based on the learned temperature preferences, the learned temperature preferences being based on the analysis of the one or more current indoor conditions and the one or more current outdoor conditions in relation to the stored historical temperature data.

7. A computing device configured for relative temperature preference learning, comprising:

a processor;

memory in electronic communication with the processor, wherein the memory stores computer executable instructions that when executed by the processor cause the processor to perform the steps of;

identifying one or more current settings of a thermostat, wherein the thermostat is a component of a heating ventilation air conditioning (HVAC) system located at a premises, wherein the one or more current settings of the thermostat include at least one of a current target temperature, current runtime settings, and current airflow settings of the HVAC system;

identifying one or more current indoor conditions, wherein the one or more current indoor conditions include at least one of a current indoor temperature, current indoor humidity, and current indoor airflow;

identifying one or more current outdoor conditions, wherein the one or more current outdoor conditions include at least one of a current outdoor temperature, a current outdoor humidity, current atmospheric pressure, current level of precipitation, and current level of cloud cover;

calculating a current indoor differential between the current indoor temperature and the current target temperature;

calculating a current outdoor differential between the current outdoor temperature and the current target temperature;

identifying historical temperature data stored in a database, the historical temperature data comprising a plurality of historical target temperatures, wherein each record of the plurality of historical target temperatures is associated with an historical indoor temperature and an historical outdoor temperature;

calculating an average indoor differential, based at least in part on a set of calculated differentials between the identified historical target temperatures and associated historical indoor temperatures;

calculating an average outdoor differential, based at least in part on a set of calculated differentials between the identified historical target temperatures and associated historical outdoor temperature;

communicating, over a time period, suggested target temperatures to an occupant of the premises;

tracking, over the time period, user acceptance of the suggested target temperatures

learning temperature preferences for the target temperature based on an analysis of the one or more current indoor conditions, the one or more current outdoor conditions, the calculated current outdoor differential, the calculated indoor differential, the calculated average outdoor differential, the calculated average indoor differential, the tracked user acceptance, and the stored historical temperature data; and

adjusting, by a temperature preference module, the current target temperature of the HVAC system based at least in part on the learned temperature preferences for the target temperature.

8. The computing device of claim 7, wherein the instructions executed by the processor cause the processor to perform the steps of:

upon determining the user acceptance of the suggested target temperatures over the time period satisfies a user acceptance threshold, automatically adjusting the current target temperature without communicating the suggested target temperatures for user acceptance.

9. The computing device of claim 7, wherein the instructions executed by the processor cause the processor to perform the steps of:

determining whether the current indoor differential satisfies an indoor differential threshold, the indoor differential threshold being based on the calculated average indoor differential.

10. The computing device of claim 9, wherein the instructions executed by the processor cause the processor to perform the steps of:

upon determining the current indoor differential satisfies the indoor differential threshold and the premises is occupied, calculating a suggested target temperature based on the learned temperature preferences, the learned temperature preferences being based on the analysis of the one or more current indoor conditions and the one or more current outdoor conditions in relation to the stored historical temperature data.

11. The computing device of claim 7, wherein the instructions executed by the processor cause the processor to perform the steps of:

determining whether the current outdoor differential satisfies an outdoor differential threshold, the outdoor differential threshold being based on the calculated average outdoor differential; and

upon determining the current outdoor differential satisfies the outdoor differential threshold and the premises is occupied, calculating a suggested target temperature based on the learned temperature preferences, the learned temperature preferences being based on the analysis of the one or more current indoor conditions and the one or more current outdoor conditions in relation to the stored historical temperature data.

12. A non-transitory computer-readable storage medium storing computer executable instructions that when executed by a processor cause the processor to perform the steps of:

identifying one or more current settings of a thermostat, wherein the thermostat is a component of a heating ventilation air conditioning (HVAC) system located at a premises, wherein the one or more current settings of the thermostat include at least one of a current target temperature, current runtime settings, and current airflow settings of the HVAC system;

identifying one or more current indoor conditions, wherein the one or more current indoor conditions include at least one of a current indoor temperature, current indoor humidity, and current indoor airflow;

identifying one or more current outdoor conditions, wherein the one or more current outdoor conditions include at least one of a current outdoor temperature, a current outdoor humidity, current atmospheric pressure, current level of precipitation, and current level of cloud cover;

calculating a current indoor differential between the current indoor temperature and the current target temperature;

calculating a current outdoor differential between the current outdoor temperature and the current target temperature; and

identifying historical temperature data stored in a database, the historical temperature data comprising a plurality of historical target temperatures, wherein each record of the plurality of historical target temperatures is associated with an historical indoor temperature and an historical outdoor temperature;

calculating an average indoor differential, based at least in part on a set of calculated differentials between the identified historical target temperatures and associated historical indoor temperatures;

calculating an average outdoor differential, based at least in part on a set of calculated differentials between the identified historical target temperatures and associated historical outdoor temperature;

communicating, over a time period, suggested target temperatures to an occupant of the premises;

tracking, over the time period, user acceptance of the suggested target temperatures;

learning temperature preferences for the target temperature based on an analysis of the one or more current indoor conditions, the one or more current outdoor conditions, the calculated current outdoor differential, the calculated indoor differential, the calculated average outdoor differential, the calculated average indoor differential, the tracked user acceptance, and the stored historical temperature data; and

adjusting, by a temperature preference module, the current target temperature of the HVAC system based at least in part on the learned temperature preferences for the target temperature.