Computer-Implemented System And Method For Externally Inferring An Effective Indoor Temperature In A Building

• US 20150276495A1
• 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, Beck, Victor
• Application Number:
  US14/228,207
• Publication Number:
  US 20150276495A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: F24F 11/00 Control or safety arrangements
• CPC Class Codes: F24F 11/46 Improving electric energy efficiency or saving
• CPC Class Codes: F24F 11/61 using timers
• CPC Class Codes: G01K 3/04 in respect of time
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 50/06 Energy or water supply

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 fit of the data is performed, by setting the energy usage data as the independent variable to which the ambient temperature data is linearly fit. A fit could be, for instance, an ordinary least squares linear regression, a frequentist regression, a Bayesian regression, or a robust regression. The ambient temperature at the intercept of the fit where the energy usage equals zero is taken as the effective thermostat set point of the indoor climate control system and serves as a surrogate for the effective indoor temperature of the building.

Claims

1. A computer-implemented method for externally inferring an effective indoor temperature in a building, 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 during which the indoor climate control system transitions from a running state to an at idle state and a “

go-to-run”

state transition 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 for the building;

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

performing a linear fit of the usage time series and the temperature time series; and

extracting the temperature at an intercept of the linear fit at which the usage equals zero as an effective indoor temperature of the building,

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

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

specifying that data in the usage time series comprises independent variables to which data in the temperature data are linearly fit.

3. 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.

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

extracting a duty cycle time series from the usage time series that reflects a percentage of each operating cycle that the indoor climate control system was running;

forming an operating cycle temperature time series by assigning a temperature from the temperature time series to each operating cycle; and

performing the linear fit on the duty cycle time series and the operating cycle temperature time series.

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

creating the operating cycle temperature time series comprising, for each operating cycle in the duty cycle time series, one of a mean temperature, a median temperature, a temperature when the indoor climate control system is running, a temperature when the indoor climate control system is at idle, a temperature at the beginning of the operating cycle, a mean temperature over a period of time that begins before the operating cycle, a temperature at a time before the operating cycle, and a temperature at the end of the operating cycle, and a temperature derived from interpolation of the temperature time series to match time stamps in the usage time series.

6. A method according to claim 4, further comprising at least one of the steps of:

removing at least one of high duty cycles and low duty cycles from the duty cycle time series, and also removing the temperatures that were assigned to the removed duty cycles from the operating cycle temperature time series, both prior to performing the linear fit;

retaining only select duty cycles in the duty cycle time series, and also retaining only the temperatures that were assigned to the retained duty cycles in the operating cycle temperature time series, both prior to performing the linear fit;

retaining only select values of the operating cycle temperature time series, and also retaining only the duty cycles in the duty cycle time series that were assigned to the select values of the operating cycle temperature time series, both prior to performing the linear fit; and

retaining duty cycles in the duty cycle time series for only select hours days, weeks, months, times of the year, or time periods, and also retaining only the temperatures that were assigned to the retained duty cycles in the operating cycle temperature time series, prior to performing the linear fit.

7. A method according to claim 1, further comprising at least one of the steps of:

scaling at least one of the usage time series and the temperature time series at each point by a function of ambient temperature; and

creating a lagged temperature variable by shifting the temperature time series by a certain amount of time.

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

applying one or more filters to at least one of the usage time series and the temperature time series; and

performing the linear fit on the at least one of the usage time series and the temperature time series as filtered.

9. A method according to claim 8, further comprising the step of:

selecting the one or more filters from the group comprising an exponential filter, a moving average filter, a weighting moving average filter, a time delay filter, a low pass filter, a band pass filter, and a noise smoothing filter.

10. A method according to claim 8, further comprising the steps of:

dividing the at least one of the usage time series and the temperature time series into a plurality of spans of time; and

choosing the one or more filters differently for each of the spans of time.

11. A method according to claim 1, further comprising at least one of the steps of:

removing at least one of long operating cycles and short operating cycles from the usage time series, and also removing the temperatures for the removed operating cycles from the temperature time series, prior to performing the linear fit;

removing operating cycles comprising select values of the duty cycle from the usage time series, and also removing the temperatures assigned to the removed operating cycles from the temperature time series, prior to performing the linear fit;

retaining only select values of the temperature time series, and also removing from the usage time series the usages assigned to the non-selected values of the temperature time series, both prior to performing the linear fit;

retaining only select operating cycles in the usage time series, and also retaining only the temperatures for the retained duty cycles in the temperature time series, prior to performing the linear fit; and

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 for the retained operating cycles in the temperature time series, prior to performing the linear fit.

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

finding the effective indoor temperatures in each of a plurality of other buildings; and

comparing the effective indoor temperature in the building against the effective indoor temperatures in the other buildings.

13. A method according to claim 12, further comprising at least one of the steps of:

choosing recommendations for reducing energy consumption with respect to the effective indoor temperature of the building;

choosing a type of energy consumption reduction offering with respect to the energy consumption of the building; and

permitting third parties to advertise or offer products or services with respect to the building.

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

assessing a change to the effective indoor temperature from one fixed period of time to one or more other fixed periods of time.

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

obtaining an indication of the indoor temperature that was taken while the indoor climate control system was operating; and

determining a difference between the indoor temperature indication and the effective indoor temperature.

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

finding the effective indoor temperatures in each of a plurality of buildings that include the building;

obtaining indications of the indoor temperature that was taken while the indoor climate control systems in each of the plurality of buildings was operating; and

comparing the buildings, comprising at least one of the steps of;

assessing either thermal performance or relative sizing of thermal loads across the buildings based on a difference between the indoor temperature indications and the effective indoor temperatures;

narrowing the set of reasons that one of the buildings may consume more energy for indoor climate control than another of the buildings based on the comparison of the difference between indoor temperature indication and the effective indoor temperature; and

assessing a change in thermal performance or relative sizing of the thermal loads by comparing from one time period to another time period the differences between the indoor temperature indications and the effective indoor temperatures.

17. A method according to claim 1, further comprising at least one of the steps of:

performing further thermal analysis of the building using the effective indoor temperature as an input;

calibrating other tools for thermal analysis of the building using the effective indoor temperature as an input;

performing further analysis of the indoor climate control system of the building using the effective indoor temperature as an input; and

assessing impact of behavioral or structural changes by comparing thermal performance using the effective indoor temperature from one time period to another.

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

defining the temperature range around the desired indoor temperature as a deadband comprising a lower temperature slightly below the desired indoor temperature and an upper temperature slightly above the desired indoor temperature.

19. A method according to claim 1, wherein the linear fit comprises a regression analysis in which the estimated relationship between the dependent variable and one or more independent variables is substantially linear in the dependent variables and other terms in the estimated relationship besides a constant term and the substantially linear terms are smaller in magnitude or negligible.

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