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    CENTRAL PLANT WITH ASSET ALLOCATOR
    203.
    发明申请
    CENTRAL PLANT WITH ASSET ALLOCATOR 审中-公开

    公开(公告)号：US20180285800A1

    公开(公告)日：2018-10-04

    申请号：US15473496

    申请日：2017-03-29

    Applicant: Johnson Controls Technology Company

    Inventor： Michael J. Wenzel ,  Matthew J. Ellis

    IPC: G06Q10/06 ,  G06Q50/06 ,  G05B13/02 ,  H02J3/38 ,  H02J13/00

    Abstract: A central plant includes an asset allocator configured to determine an optimal allocation of energy loads across central plant equipment. The asset allocator identifies sources configured to supply input resources, subplants configured to convert the input resources to output resources, and sinks configured to consume the output resources. The asset allocator generates a cost function and a resource balance constraint. The resource balance constraint requires balance between a total amount of each resource supplied by the sources and the subplants and a total amount of each resource consumed by the subplants and the sinks. The asset allocator determines the optimal allocation of the energy loads across the central plant equipment by optimizing the cost function subject to the resource balance constraint. The asset allocator is configured to control the central plant equipment to achieve the optimal allocation of the energy loads.

    System identification and model development
    204.
    发明授权
    System identification and model development 有权

    公开(公告)号：US10088814B2

    公开(公告)日：2018-10-02

    申请号：US14970187

    申请日：2015-12-15

    Applicant: Johnson Controls Technology Company

    Inventor： Michael J. Wenzel ,  Robert D. Turney

    IPC: G06F17/50 ,  G05B13/02

    Abstract: A controller for a building system receives training data that includes input data and output data. The output data measures a state of the building system affected by both the input data and an extraneous disturbance. The controller performs a two-stage optimization process to identify system parameters and Kalman gain parameters of a dynamic model for the building system. During the first stage, the controller filters the training data to remove an effect of the extraneous disturbance from the output data and uses the filtered training data to identify the system parameters. During the second stage, the controller uses the non-filtered training data to identify the Kalman gain parameters. The controller uses the dynamic model with the identified system parameters and Kalman gain parameters to generate a setpoint for the building system. The building system uses the setpoint to affect the state measured by the output data.

    BUILDING ENERGY COST OPTIMIZATION SYSTEM WITH ASSET SIZING
    205.
    发明申请
    BUILDING ENERGY COST OPTIMIZATION SYSTEM WITH ASSET SIZING 审中-公开

    公开(公告)号：US20180224814A1

    公开(公告)日：2018-08-09

    申请号：US15426962

    申请日：2017-02-07

    Applicant: Johnson Controls Technology Company

    Inventor： Mohammad N. Elbsat ,  Michael J. Wenzel

    IPC: G05B17/02 ,  G06Q30/02 ,  F24F11/00

    CPC classification number: G05B17/02 ,  F24F11/30 ,  F24F11/46 ,  F24F11/47 ,  F24F2140/50 ,  G05B13/021 ,  G06Q30/0283

    Abstract: An energy cost optimization system for a building includes HVAC equipment and a controller. The controller is configured to generate a cost function defining a cost of operating the HVAC equipment as a function of one or more energy load setpoints. The controller is configured to modify the cost function to account for both an initial purchase cost of a new asset to be added to the HVAC equipment and an effect of the new asset on the cost of operating the HVAC equipment. Both the initial purchase cost of the new asset and the effect of the new asset on the cost of operating the HVAC equipment are functions of one or more asset size variables. The controller is configured to perform an optimization using the modified cost function to determine optimal values for decision variables including the energy load setpoints and the asset size variables.

    SYSTEMS AND METHODS FOR ADAPTIVELY UPDATING EQUIPMENT MODELS
    207.
    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
    208.
    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.

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