Computer-Implemented System And Method For Externally Evaluating Thermostat Adjustment Patterns Of An Indoor Climate Control System In A Building

• US 20150276508A1
• Filed: 03/27/2014
• Published: 10/01/2015
• Est. Priority Date: 03/27/2014
• Status: Abandoned Application

• Current Assignee: Palo Alto Research Center, Inc. (Xerox Holdings Corp.)
• Original Assignee: Palo Alto Research Center, Inc. (Xerox Holdings Corp.)
• Inventors: Smullin, Sylvia
• Application Number:
  US14/228,211
• Publication Number:
  US 20150276508A1
• Time in Patent Office: Days
• US Class Current: 1/1
• 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 11/56 Remote control
• CPC Class Codes: F24F 11/61 using timers
• 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 2120/00 Control inputs relating to users or occupants
• CPC Class Codes: G01H 1/00 Measuring characteristics of vibrations in solids by using direct conduction to the detector G01H9/00, G01H11/00 take precedence
• CPC Class Codes: G01H 3/10 Amplitude; Power
• CPC Class Codes: G01J 5/10 using electric radiation detectors
• CPC Class Codes: G01K 13/00 Thermometers specially adapted for specific purposes
• CPC Class Codes: G01N 25/56 by investigating moisture content
• CPC Class Codes: G01N 33/004 CO or CO2
• CPC Class Codes: G01R 21/00 Arrangements for measuring electric power or power factor G01R7/12 takes precedence
• CPC Class Codes: G05D 23/1904 variable in time
• CPC Class Codes: G06Q 10/10 Office automation; Time management

Abstract

Energy usage data of an indoor climate control system, such as an HVAC system, for a building and ambient temperature data are obtained for a time period of interest with a time resolution that reflects the physically relevant time scales. The data are formed into time series. A correlation between the system'"'"'s usage and ambient temperature is established, where a strong (or high) correlation is interpreted as an indication that the thermostat set point infrequently gets changed, if at all, whilst a weak (or low) correlation is interpreted as an indication that the thermostat set point is changed regularly. In addition, a correlation between the system'"'"'s usage and the building'"'"'s occupancy can be established, which can help corroborate the assessment of the appropriateness or efficiency of thermostat set point changing patterns.

Claims

1. A computer-implemented method for externally evaluating thermostat adjustment patterns of an indoor climate control system in a building based on temperature, comprising the steps of:

obtaining a usage time series that reflects indoor climate control system usage in a building over a plurality of operating cycles, each operating cycle comprising a “

go-to-idle”

state transition triggered by a thermostat during which the indoor climate control system transitions from a running state to an at idle state and a “

go-to-run”

state transition triggered by the thermostat during which the indoor climate control system transitions from an at idle state to a running state, the indoor climate control system running for a period of running time between each “

go-to-run”

state transition and the next “

go-to-idle”

state transition, the indoor climate control system remaining at idle for a period of idle time between each “

go-to-idle”

state transition and the next “

go-to-run”

state transition, the running time comprising the time necessary to bring the building'"'"'s interior temperature into a temperature range defined about a desired indoor temperature as specified by a set point of the thermostat for the building;

obtaining a temperature time series for the temperature ambient to the building over the same plurality of the operating cycles; and

finding a temperature correlation between the usage time series and the temperature time series, wherein a low temperature correlation is interpreted as changing of the thermostat set point during the operating cycles,

wherein the steps are performed on a suitably-programmed computer.

2. A method according to claim 1, further comprising the step of:

defining the usage time series as binary indications of whether the indoor climate control system is running or at idle at any given time.

3. A method according to claim 1, further comprising the steps of:

selecting a big time unit that is larger than the time unit used in the usage time series;

dividing the usage time series into increments of the big time unit;

forming groups of the usage data in the usage time series within each increment of the big time unit and finding characteristic usages that are representative of the usage data in each of the usage data groups;

forming groups of the temperature data in the temperature time series within each increment of the big time unit and finding characteristic temperatures that are representative of the temperature data in each of the temperature data groups; and

defining the temperature correlation as a function of the characteristic usage of each of the usage data groups and the characteristic temperature of each of the temperature data groups.

4. A method according to claim 1, further comprising the step of:

choosing an energy consumption reduction offering with respect to the changing of the thermostat set point of the building.

5. A method according to claim 4, further comprising the steps of:

awarding the energy consumption reduction offering;

re-determining the changing of the thermostat set point subsequent to the awarding of the energy consumption reduction offering; and

comparing the changing of the thermostat set point as originally determined and the changing of the thermostat set point as re-determined.

6. A method according to claim 1, further comprising the steps of:

obtaining energy usage for the indoor climate control systems of the building and other buildings;

comparing the energy usage of the building to the energy usage of the other buildings; and

upon finding that the energy usage of the building is higher than the energy usage of the other buildings, assessing whether the changing of the thermostat set point is a contributor to the building'"'"'s higher energy usage.

7. A method according to claim 1, further comprising the step of:

permitting third parties to advertise or offer products or services with respect to the changing of the thermostat set point.

8. A non-transitory computer readable storage medium storing code for executing on a computer system to perform the method according to claim 1.

9. A computer-implemented method for externally evaluating thermostat adjustment patterns of an indoor climate control system in a building based on temperature and occupancy, comprising the steps of:

obtaining a usage time series that reflects indoor climate control system usage in a building over a plurality of operating cycles, each operating cycle comprising a “

go-to-idle”

state transition triggered by a thermostat during which the indoor climate control system transitions from a running state to an at idle state and a “

go-to-run”

state transition triggered by the thermostat during which the indoor climate control system transitions from an at idle state to a running state, the indoor climate control system running for a period of running time between each “

go-to-run”

state transition and the next “

go-to-idle”

state transition, the indoor climate control system remaining at idle for a period of idle time between each “

go-to-idle”

state transition and the next “

go-to-run”

state transition, the running time comprising the time necessary to bring the building'"'"'s interior temperature into a temperature range defined about a desired indoor temperature as specified by a set point of the thermostat for the building;

obtaining a temperature time series for the temperature ambient to the building over the same plurality of the operating cycles;

obtaining an occupancy time series for occupancy of the building over the same plurality of the operating cycles;

finding a temperature correlation between the usage time series and the temperature time series, wherein a low temperature correlation is interpreted as changing of the thermostat set point during the operating cycles; and

finding an occupancy correlation between the usage time series and the occupancy time series, wherein a high occupancy correlation coupled with the low temperature correlation is interpreted as the thermostat set point changing with changing of the occupancy of the building,

wherein the steps are performed on a suitably-programmed computer.

10. A method according to claim 9, further comprising the steps of:

selecting a big time unit that is larger than the time unit used in the usage time series;

dividing the usage time series into increments of the big time unit;

forming groups of the usage data in the usage time series within each increment of the big time unit and finding characteristic usages that are representative of the usage data in each of the usage data groups;

forming groups of the temperature data in the temperature time series within each increment of the big time unit and finding characteristic occupancies that are representative of the temperature data in each of the temperature data groups;

forming groups of the occupancy data in the occupancy time series within each increment of the big time unit and finding characteristic occupancies that are representative of the occupancy data in each of the occupancy data groups;

defining the temperature correlation as a function of the characteristic usage of each of the usage data groups and the characteristic temperature of each of the temperature data groups; and

defining the occupancy correlation as a function of the characteristic usage of each of the usage data groups and the characteristic occupancy of each of the occupancy data groups.

11. A method according to claim 10, further comprising of the steps of:

defining a threshold of usage change;

defining a threshold of temperature change;

defining a threshold of occupancy change;

identifying each of the times that the characteristic usage of each of the usage data groups comprised in consecutive increments of the big time unit exceed the usage change threshold as a series of usage changes;

identifying each of the times that the characteristic temperature of each of the temperature data groups comprised in consecutive increments of the big time unit exceed the temperature change threshold as a series of temperature changes;

identifying each of the times that the characteristic occupancy of each of the occupancy data groups comprised in consecutive increments of the big time unit exceed the occupancy change threshold as a series of occupancy changes;

comparing the usage changes series to the temperature changes series as the temperature correlation; and

comparing the usage changes series to the occupancy changes series as the occupancy correlation.

12. A method according to claim 9, further comprising the step of:

retaining operating cycles in the usage time series for only select hours days, weeks, months, times of the year, or time periods, and also retaining only the temperatures in the temperature time series and only the occupancies in the occupancy time series that both correspond to the times of the retained operating cycles in the usage time series, prior to finding the temperature correlation and the occupancy correlation.

13. A method according to claim 9, wherein the temperature correlation comprises at least one of a Pearson product-moment correlation coefficient, a rank correlation coefficient, a multi-moment correlation coefficient, a Brownian or distance correlation coefficient, a coefficient of correlation between two time series in which one of the time series comprises a time delay relative to the other time series, and a coefficient of correlation between a finite difference or derivative of one of the time series and a finite difference or derivative of the other time series, and

the occupancy correlation comprises at least one of a Pearson product-moment correlation coefficient, a rank correlation coefficient, a multi-moment correlation coefficient, a Brownian or distance correlation coefficient, a coefficient of correlation between two time series in which one of the time series comprises a time delay relative to the other time series, and a coefficient of correlation between a finite difference or derivative of one of the time series and a finite difference or derivative of the other time series.

14. A method according to claim 9, further comprising the steps of:

finding a thermostat set point frequency of the changing of the thermostat set point;

finding an occupancy frequency of changing of occupancy of the building; and

correlating the thermostat set point frequency to the occupancy frequency.

15. A method according to claim 9, further comprising the step of:

choosing an energy consumption reduction offering with respect to the changing of the thermostat set point of the building.

16. A method according to claim 15, further comprising the step of:

targeting the energy consumption reduction offering to occupants of the building when the assessed thermostat set point changing fails to occur regularly with the changing of the occupancy of the building.

17. A method according to claim 9, further comprising determining the occupancy of the building by performing at least one of the steps of:

including only occupants of the building who are awake or exhibiting a level of activity as part of the occupancy of the building;

sensing occupancy or changes in the occupancy of the building, comprising at least one of the steps of;

providing an entry sensor in an entryway configured to sense ingress and egress of occupants of the building;

providing a motion sensor within the building configured to sense movement of the occupants of the building;

providing an infrared sensor configured to sense heat radiated from the occupants of the building;

providing a humidity sensor in the building configured to sense humidity generated by the occupants of the building;

providing a noise level sensor in the building configured to detect noise created by the occupants of the building;

providing a carbon dioxide sensor in the building configured to detect carbon dioxide created by the occupants of the building; and

providing a vibration sensor in the building configured to detect vibrations generated by the occupants of the building;

tracking a frequency with which appliances in the building are operated as an indication of the occupancy;

tracking energy usage in the building as an indication of the occupancy;

tracking wireless network usage in the building as an indication of the occupancy;

obtaining demographic data representing the occupancy of the building; and

determining occupancy from entry badge, electronic key, or computer network authorizations within the building.

18. A non-transitory computer readable storage medium storing code for executing on a computer system to perform the method according to claim 9.