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    Model predictive maintenance system with degradation impact model
    1.
    发明授权
    Model predictive maintenance system with degradation impact model 有权

    公开(公告)号：US12282324B2

    公开(公告)日：2025-04-22

    申请号：US16899220

    申请日：2020-06-11

    Applicant: Johnson Controls Technology Company

    Inventor： Mohammad N. Elbsat ,  Michael J. Wenzel ,  Robert D. Turney

    IPC: G05B23/02 ,  G05B19/042 ,  G06N3/08

    Abstract: A model predictive maintenance (MPM) system for building equipment includes one or more processing circuits having one or more processors and memory. The memory store instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including estimating a degradation state of the building equipment, using a degradation impact model to predict an amount of one or more input resources consumed by the building equipment to produce one or more output resources based on the degradation state of the building equipment, generating a maintenance schedule for the building equipment based on the amount of the one or more input resources predicted by the degradation impact model, and initiating a maintenance activity for the building equipment in accordance with the maintenance schedule.

    HVAC control system with model driven deep learning
    3.
    发明授权
    HVAC control system with model driven deep learning 有权

    公开(公告)号：US11507033B2

    公开(公告)日：2022-11-22

    申请号：US16413946

    申请日：2019-05-16

    Applicant: Johnson Controls Technology Company

    Inventor： Robert D. Turney ,  Henry O. Marcy, V

    IPC: G05B13/02 ,  G06N3/08 ,  G06F30/20

    Abstract: A method includes operating equipment to affect a variable state or condition of a space and determining a set of learned weights for a neural network by modeling an estimated cost of operating the equipment over a plurality of simulated scenarios. Each simulated scenario includes simulated measurements relating to the space. The neural network is configured to generate simulated control dispatches for the equipment based on the simulated measurements. The method also includes configuring the neural network for online control by applying the set of learned weights, applying actual measurements relating to the space to the neural network to generate a control dispatch for the equipment, and controlling the equipment in accordance with the control dispatch.

    System identification and model development
    7.
    发明授权
    System identification and model development 有权

    公开(公告)号：US11086276B2

    公开(公告)日：2021-08-10

    申请号：US16144192

    申请日：2018-09-27

    Applicant: Johnson Controls Technology Company

    Inventor： Michael J. Wenzel ,  Robert D. Turney

    IPC: G05B13/02 ,  G05B13/04 ,  G05B15/02 ,  G06F30/00 ,  G06F30/13

    Abstract: A controller for a building system receives training data including input data and output data. The output data indicate a state of the building system affected by the input data. The controller pre-processes the training data using a first set of pre-processing options to generate a first set of training data and pre-processes the training data using a second set of pre-processing options to generate a second set of training data. The controller performs a multi-stage optimization process to identify multiple different sets of model parameters of a dynamic model for the building system. The multi-stage optimization process includes a first stage in which the controller uses the first set of training data to identify a first set of model parameters and a second stage in which the controller uses the second set of training data to identify a second set of model parameters. The controller uses the dynamic model to operate the building system.

    HVAC control system with cost target optimization
    8.
    发明授权
    HVAC control system with cost target optimization 有权

    公开(公告)号：US11009252B2

    公开(公告)日：2021-05-18

    申请号：US16403924

    申请日：2019-05-06

    Applicant: Johnson Controls Technology Company

    Inventor： Robert D. Turney ,  Jiaqi Li

    IPC: F24F11/63 ,  F24F11/52 ,  F24F11/47 ,  F24F110/10

    Abstract: A building management system includes HVAC equipment operable to affect an indoor air temperature of a building, a system manager configured to obtain a cost function that characterizes a cost of operating the HVAC equipment, obtain a dataset relating to the building, determine a current state of the building by applying the dataset to a neural network, select a temperature bound associated with the current state, augment the cost function to include a penalty term that increases the cost when the indoor air temperature violates the temperature bound, and determine a temperature setpoint for each of a plurality of time steps in the future time period. The temperature setpoints achieve a target value of the cost function over the future time period. The building management system also includes a controller configured to operate the HVAC equipment to drive the indoor air temperature towards the temperature setpoint.

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