Patent Document ID: 9778639
Application ID: 14579736
Patent Status: 1

Claim One:
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.