Systems and methods for adaptively updating equipment models

Patent metadata

Field	Value
Publication number	US-2016180220-A1
Application number	US-201414579736-A
Country	US
Kind code	A1
Filing date	Dec 22, 2014
Priority date	Dec 22, 2014
Publication date	Jun 23, 2016
Grant date	—

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.

First claim

What is claimed is:

1 . A system for adaptively updating a predictive model for building equipment or a collection of building equipment, the system comprising:
an operating data aggregator module configured to collect 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; a model generator module configured to generate 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 configured to generate 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 configured to calculate a critical value for the test statistic; a hypothesis testing module configured to perform a statistical hypothesis test comprising comparing the test statistic with the critical value to determine whether the predictive model has changed; and a model update module configured to adaptively update the predictive model in response to a determination that the test statistic exceeds the critical value.

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 is configured to:
adjust 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 update 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 configured to use the updated predictive model to generate a control output for the building equipment using a model-based control methodology.

7 . The system of claim 1 , further comprising an autocorrelation corrector configured to remove an autocorrelated model error from at least one of the first set of operating data and the second set of operating data prior to the model generator module determining the sets of model coefficients.

8 . The system of claim 7 , 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.

9 . A system for detecting a fault in a predictive model for building equipment or a collection of building equipment, the system comprising:
an operating data aggregator module configured to collect 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; a model generator module configured to generate 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 configured to generate 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 configured to calculate a critical value for the test statistic; a hypothesis testing module configured to perform a statistical hypothesis test comprising comparing the test statistic with the critical value to determine whether the predictive model has changed; and a fault detection module configured to generate a fault indication in response to a determination that the test statistic exceeds the critical value.

10 . The system of claim 9 , 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.

11 . The system of claim 9 , 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.

12 . The system of claim 9 , wherein the model generator module is configured to:
adjust 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 update 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.

13 . The system of claim 9 , further comprising a demand response module configured to use the updated predictive model to generate a control output for the building equipment using a model-based control methodology.

14 . The system of claim 9 , further comprising an autocorrelation corrector configured to remove an autocorrelated model error from at least one of the first set of operating data and the second set of operating data prior to the model generator module determining the sets of model coefficients.

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

• Johnson Controls Tech Co

Inventors

• Boettcher Andrew J
• Vitullo Steven R
• Drees Kirk H
• Wenzel Michael J

Classifications

• G05B2219/2614

  HVAC, heating, ventillation, climate control · CPC title

• G05B19/042 Primary

  using digital processors (G05B19/05 takes precedence) · CPC title

• G06N5/022 Primary

  Knowledge engineering; Knowledge acquisition · CPC title

• G05B23/024 Primary

  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 · CPC title

• G05B2219/2642

  Domotique, domestic, home control, automation, smart house · CPC title