Systems and methods for adaptively updating equipment models
First Claim
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.
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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.
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Citations
16 Claims
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1. A system for adaptively updating a predictive model for building equipment or a collection of building equipment, the system comprising:
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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. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A system for detecting a fault in a predictive model for building equipment or a collection of building equipment, the system comprising:
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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. - View Dependent Claims (8, 9, 10, 11)
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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:
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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. - View Dependent Claims (13, 14, 15, 16)
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Specification