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    BUILDING MANAGEMENT SYSTEM WITH AUGMENTED DEEP LEARNING USING COMBINED REGRESSION AND ARTIFICIAL NEURAL NETWORK MODELING
    111.
    发明申请
    BUILDING MANAGEMENT SYSTEM WITH AUGMENTED DEEP LEARNING USING COMBINED REGRESSION AND ARTIFICIAL NEURAL NETWORK MODELING 审中-公开

    公开(公告)号：US20190041811A1

    公开(公告)日：2019-02-07

    申请号：US16054805

    申请日：2018-08-03

    Applicant: Johnson Controls Technology Company

    Inventor： Kirk H. Drees

    IPC: G05B13/04 ,  G06N3/08 ,  G06N7/00 ,  G06K9/62

    Abstract: A building management system is provided. The building management system includes a database, a trust region identifier configured to perform a cluster analysis technique to identify trust regions, and a regression model predictor configured to utilize a regression model technique to calculate a regression model prediction. The building management system further includes a distance metric calculator configured to calculate a distance metric, an artificial neural network model predictor configured to utilize an artificial neural network model technique to calculate an artificial neural network model prediction, and a combined prediction calculator configured to determine a combined prediction based on the distance metric, the regression model prediction, and the artificial neural network model prediction.

    SYSTEMS AND METHODS FOR ADAPTIVELY UPDATING EQUIPMENT MODELS
    114.
    发明申请
    SYSTEMS AND METHODS FOR ADAPTIVELY UPDATING EQUIPMENT MODELS 审中-公开

    公开(公告)号：US20180011459A1

    公开(公告)日：2018-01-11

    申请号：US15694033

    申请日：2017-09-01

    Applicant: Johnson Controls Technology Company

    Inventor： Andrew J. Boettcher ,  Steven R. Vitullo ,  Kirk H. Drees ,  Michael J. Wenzel

    IPC: G05B19/042 ,  G05B23/02

    CPC classification number: G05B19/042 ,  G05B23/024 ,  G05B2219/2642

    Abstract: A system for generating and using a predictive model to control building equipment includes building equipment operable to affect one or more variables in a building and an operating data aggregator that collects a set of operating data for the building equipment. The system includes an autocorrelation corrector that removes an autocorrelated model error from the set of operating data by 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 an autocorrelation for the model error, and transforming the set of operating data using the autocorrelation. The system includes a model generator module that generates a set of model coefficients for the predictive model using the transformed set of operating data and a controller that controls the building equipment by executing a model-based control strategy that uses the predictive model.

    Systems and methods for adaptively updating equipment models
    115.
    发明授权
    Systems and methods for adaptively updating equipment models 有权

    公开(公告)号：US09778639B2

    公开(公告)日：2017-10-03

    申请号：US14579736

    申请日：2014-12-22

    Applicant: Johnson Controls Technology Company

    Inventor： Andrew J. Boettcher ,  Steven R. Vitullo ,  Kirk H. Drees ,  Michael J. Wenzel

    IPC: G05B19/042 ,  G05B23/02

    CPC classification number: G05B19/042 ,  G05B23/024 ,  G05B2219/2642

    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.

    PHOTOVOLTAIC ENERGY SYSTEM WITH SOLAR INTENSITY PREDICTION
    117.
    发明申请
    PHOTOVOLTAIC ENERGY SYSTEM WITH SOLAR INTENSITY PREDICTION 审中-公开

    公开(公告)号：US20170104449A1

    公开(公告)日：2017-04-13

    申请号：US15247869

    申请日：2016-08-25

    Applicant: Johnson Controls Technology Company

    Inventor： Kirk H. Drees

    IPC: H02S50/00 ,  H02J7/35 ,  H02J3/38

    Abstract: A photovoltaic energy system includes a photovoltaic field configured to convert solar energy into electrical energy, one or more solar intensity sensors configured to measure solar intensity and detect a cloud approaching the photovoltaic field, and a controller. The controller receives input from the solar intensity sensors and predicts a change in solar intensity within the photovoltaic field before the change in solar intensity within the photovoltaic field occurs. The controller is configured to preemptively adjust an electric power output of the photovoltaic energy system in response to predicting the change in solar intensity within the photovoltaic field.

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