Systems and methods for adaptively updating equipment models

• US 9,778,639 B2
• Filed: 12/22/2014
• Issued: 10/03/2017
• Est. Priority Date: 12/22/2014
• Status: Active Grant

• Current Assignee: Tyco Fire & Security GmbH (Johnson Controls International plc)
• Original Assignee: Johnson Controls Technology Company (Johnson Controls International plc)
• Inventors: Boettcher, Andrew J., Vitullo, Steven R., Drees, Kirk H., Wenzel, Michael J.
• Primary Examiner(s): Tran, Vincent
• Application Number:
  US14/579,736
• Publication Number:
  US 20160180220A1
• Time in Patent Office: 1,016 Days
• Field of Search: 700276
• CPC Class Codes: G05B 19/042 using digital processors G05B19/05 takes precedence
• CPC Class Codes: G05B 2219/2642 Domotique, domestic, home control, automation, smart house
• CPC Class Codes: G05B 23/024 Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks

Abstract

An operating data aggregator module collects a first set of operating data and a second set of operating data for building equipment. A model generator module generates a first set of model coefficients and a second set of model coefficients for a predictive model for the building equipment using the first set of operating data and the second set of operating data, respectively. A test statistic module generates a test statistic based on a difference between the first set of model coefficients and the second set of model coefficients. A critical value module calculates critical value for the test statistic. A hypothesis testing module compares the test statistic with the critical value using a statistical hypothesis test to determine whether the predictive model has changed. In response to a determination that the predictive model has changed, a fault indication may be generated and/or the predictive model may be adaptively updated.

Claims

1. A system for adaptively updating a predictive model for building equipment or a collection of building equipment, the system comprising:

building equipment that operate to affect one or more variables in a building;

an operating data aggregator module that collects a first set of operating data for the building equipment corresponding to a first time period and a second set of operating data for the building equipment corresponding to a second time period;

an autocorrelation corrector that removes an autocorrelated model error from at least one of the first set of operating data and the second set of operating data, wherein removing the autocorrelated model error comprises;

determining a residual error representing a difference between an actual output of the building equipment and an output predicted by the predictive model;

using the residual error to calculate a lag one autocorrelation for the model error; and

transforming at least one of the first set of operating data and the second set of operating data using the lag one autocorrelation;

a model generator module that generates a first set of model coefficients for the predictive model using the first set of operating data and a second set of model coefficients for the predictive model using the second set of operating data;

a test statistic module that generates a test statistic based on a difference between the first set of model coefficients and the second set of model coefficients;

a critical value module that calculates a critical value for the test statistic;

a hypothesis testing module that performs a statistical hypothesis test comprising comparing the test statistic with the critical value to determine whether the predictive model has changed;

a model update module that adaptively updates the predictive model in response to a determination that the test statistic exceeds the critical value; and

a controller that uses the predictive model to control the building equipment by executing a model-based control strategy that uses the predictive model.

2. The system of claim 1, wherein adaptively updating the predictive model comprises:

generating a new set of model coefficients for the predictive model;

determining whether the new set of model coefficients improves a fit of the predictive model to a set of operating data relative to a previous set of model coefficients used in the predictive model; and

replacing the previous set of model coefficients with the new set of model coefficients in the predictive model in response to a determination that the new set of model coefficients improves the fit of the predictive model.

3. The system of claim 1, wherein adaptively updating the predictive model comprises:

generating a new set of model coefficients for the predictive model;

determining whether the new set of model coefficients improves a fit of the predictive model to a set of operating data relative to a previous set of model coefficients used in the predictive model; and

retaining the previous set of model coefficients in the predictive model in response to a determination that the new set of model coefficients does not improve the fit of the predictive model.

4. The system of claim 1, wherein calculating the critical value comprises:

identifying a parameter representing a predetermined likelihood that the statistical hypothesis test improperly rejects a null hypothesis that the predictive model has not changed; and

using an inverse cumulative distribution function for the test statistic to determine, based on the parameter, the critical value for the test statistic such that the test statistic has the predetermined likelihood of exceeding the critical value when the predictive model has not changed.

5. The system of claim 1, wherein the model generator module:

adjusts at least one of the first time period and the second time period to define an adjusted time period based on a current time; and

iteratively updates at least one of the first set of model coefficients and the second set of model coefficients using a set of the operating data corresponding to the adjusted time period.

6. The system of claim 1, further comprising a demand response module that uses the updated predictive model to generate a control output for the building equipment using a model-based control methodology.

7. A system for detecting a fault in a predictive model for building equipment or a collection of building equipment, the system comprising:

building equipment that operate to affect one or more variables in a building;

an operating data aggregator module that collects a first set of operating data for the building equipment corresponding to a first time period and a second set of operating data for the building equipment corresponding to a second time period;

an autocorrelation corrector that removes an autocorrelated model error from at least one of the first set of operating data and the second set of operating data, wherein removing the autocorrelated model error comprises;

determining a residual error representing a difference between an actual output of the building equipment and an output predicted by the predictive model;

using the residual error to calculate a lag one autocorrelation for the model error; and

transforming at least one of the first set of operating data and the second set of operating data using the lag one autocorrelation;

a model generator module that generates a first set of model coefficients for the predictive model using the first set of operating data and a second set of model coefficients for the predictive model using the second set of operating data;

a test statistic module that generates a test statistic based on a difference between the first set of model coefficients and the second set of model coefficients;

a critical value module that calculates a critical value for the test statistic;

a hypothesis testing module that performs a statistical hypothesis test comprising comparing the test statistic with the critical value to determine whether the predictive model has changed;

a fault detection module that generates a fault indication in response to a determination that the test statistic exceeds the critical value; and

a controller that uses the predictive model to control the building equipment by executing a model-based control strategy that uses the predictive model.

8. The system of claim 7, wherein generating the fault indication comprises:

generating a fault event indicating that the predictive model has changed; and

appending to the fault event a statistical confidence that the predictive model has changed, the statistical confidence based on a parameter of the statistical hypothesis test.

9. The system of claim 7, wherein calculating the critical value comprises:

identifying a parameter representing a predetermined likelihood that the statistical hypothesis test improperly rejects a null hypothesis that the predictive model has not changed; and

using an inverse cumulative distribution function for the test statistic to determine, based on the parameter, the critical value for the test statistic such that the test statistic has the predetermined likelihood of exceeding the critical value when the predictive model has not changed.

10. The system of claim 7, wherein the model generator module:

adjusts at least one of the first time period and the second time period to define an adjusted time period based on a current time; and

iteratively updates at least one of the first set of model coefficients and the second set of model coefficients using a set of the operating data corresponding to the adjusted time period.

11. The system of claim 7, further comprising a demand response module that uses the updated predictive model to generate a control output for the building equipment using a model-based control methodology.

12. A method for identifying changes in a predictive model for building equipment or a system including a collection of building equipment, the method comprising:

operating building equipment to affect one or more variables in a building;

collecting, by an operating data aggregator module of an equipment model adaptation system, a first set of operating data for the building equipment corresponding to a first time period and a second set of operating data for the building equipment corresponding to a second time period;

removing an autocorrelated model error from at least one of the first set of operating data and the second set of operating data, wherein removing the autocorrelated model error comprises;

determining a residual error representing a difference between an actual output of the building equipment and an output predicted by the predictive model;

using the residual error to calculate a lag one autocorrelation for the model error; and

transforming at least one of the first set of operating data and the second set of operating data using the lag one autocorrelation;

generating, by a model generator module of the equipment model adaptation system, a first set of model coefficients for the predictive model using the first set of operating data and a second set of model coefficients for the predictive model using the second set of operating data;

generating, by a test statistic module of the equipment model adaptation system, a test statistic based on a difference between the first set of model coefficients and the second set of model coefficients;

calculating, by a critical value module of the equipment model adaptation system, a critical value for the test statistic;

comparing, by a hypothesis testing module of the equipment model adaptation system, the test statistic with the critical value using a statistical hypothesis test to determine whether the predictive model has changed;

in response to the test statistic exceeding the critical value, triggering an action performed by the equipment model adaptation system, the action comprising at least one of generating a fault indication and adaptively updating the predictive model; and

using the predictive model to control the building equipment by executing a model-based control strategy that uses the predictive model.

13. The method of claim 12, wherein the action comprises adaptively updating the predictive model, wherein adaptively updating the predictive model comprises:

generating a new set of model coefficients for the predictive model;

determining whether the new set of model coefficients improves a fit of the predictive model to a set of operating data relative to a previous set of model coefficients used in the predictive model;

replacing the previous set of model coefficients with the new set of model coefficients in the predictive model in response to a determination that the new set of model coefficients improves the fit of the predictive model; and

retaining the previous set of model coefficients in the predictive model in response to a determination that the new set of model coefficients does not improve the fit of the predictive model.

14. The method of claim 12, wherein the action comprises generating the fault indication, wherein generating the fault indication comprises:

generating a fault event indicating that the predictive model has changed; and

appending to the fault event a statistical confidence that the predictive model has changed, the statistical confidence based on a parameter of the statistical hypothesis test.

15. The method of claim 12, wherein the test statistic is an F-statistic based on the difference between the first set of model coefficients and the second set of model coefficients.

16. The method of claim 12, wherein calculating the critical value comprises:

identifying a parameter representing a predetermined likelihood that the statistical hypothesis test improperly rejects a null hypothesis that the predictive model has not changed; and

using an inverse cumulative distribution function for the test statistic to determine, based on the parameter, the critical value for the test statistic such that the test statistic has the predetermined likelihood of exceeding the critical value when the predictive model has not changed.