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    Systems and methods for energy cost optimization in a building system
    22.
    发明授权
    Systems and methods for energy cost optimization in a building system 有权
    建筑系统能源成本优化的系统和方法

    公开(公告)号：US09436179B1

    公开(公告)日：2016-09-06

    申请号：US13802279

    申请日：2013-03-13

    Applicant: Johnson Controls Technology Company

    Inventor： Robert D. Turney ,  Michael J. Wenzel

    IPC: G05D3/12 ,  G05D5/00 ,  G05D9/00 ,  G05D11/00 ,  G05D17/00 ,  G01R11/56 ,  G01R21/133 ,  G06F17/00 ,  G05B23/02

    CPC classification number: G05B23/02 ,  G05B15/02 ,  G05B19/042 ,  G05B2219/2639 ,  G05B2219/2642 ,  Y02B70/32 ,  Y02B70/3225 ,  Y02B70/3241 ,  Y04S20/20 ,  Y04S20/222 ,  Y04S20/224 ,  Y04S20/40 ,  Y04S20/44 ,  Y04S20/46

    Abstract: Methods and systems to minimize energy cost in response to time-varying energy prices are presented for a variety of different pricing scenarios. A cascaded model predictive control system is disclosed comprising an inner controller and an outer controller. The inner controller controls power use using a derivative of a temperature setpoint and the outer controller controls temperature via a power setpoint or power deferral. An optimization procedure is used to minimize a cost function within a time horizon subject to temperature constraints, equality constraints, and demand charge constraints. Equality constraints are formulated using system model information and system state information whereas demand charge constraints are formulated using system state information and pricing information. A masking procedure is used to invalidate demand charge constraints for inactive pricing periods including peak, partial-peak, off-peak, critical-peak, and real-time.

    Abstract translation: 针对各种不同的定价情况，提出了响应时变能源价格最小化能源成本的方法和系统。 公开了一种级联模型预测控制系统，其包括内部控制器和外部控制器。 内部控制器使用温度设定值的导数控制功率使用，外部控制器通过功率设定值或功率延迟来控制温度。 优化程序用于在受到温度约束，等式约束和需求电荷约束的时间范围内最小化成本函数。 使用系统模型信息和系统状态信息制定平等约束，而使用系统状态信息和定价信息来制定需求收费约束。 屏蔽程序用于使包括峰值，部分峰值，非高峰，临界峰值和实时在内的非活动定价周期的需求电荷约束无效。

    INCORPORATING A LOAD CHANGE PENALTY IN CENTRAL PLANT OPTIMIZATION
    23.
    发明申请
    INCORPORATING A LOAD CHANGE PENALTY IN CENTRAL PLANT OPTIMIZATION 审中-公开
    在中央工厂优化中加入负担变更惩罚

    公开(公告)号：US20150316946A1

    公开(公告)日：2015-11-05

    申请号：US14634573

    申请日：2015-02-27

    Applicant: Johnson Controls Technology Company

    Inventor： Michael J. Wenzel ,  Robert D. Turney

    IPC: G05F1/66 ,  G05B13/02

    CPC classification number: G05B19/418 ,  G05B13/021 ,  G05B13/048 ,  G05B15/02 ,  G05B2219/2642 ,  G05B2219/31414 ,  G05B2219/32021 ,  G05F1/66 ,  G06N99/005 ,  G06Q10/04 ,  G06Q10/06 ,  G06Q50/06 ,  Y02P70/161 ,  Y02P80/114 ,  Y02P90/86

    Abstract: An optimization system for a central plant includes a processing circuit configured to receive load prediction data indicating building energy loads and utility rate data indicating a price of one or more resources consumed by equipment of the central plant to serve the building energy loads. The optimization system includes a high level optimization module configured to generate an objective function that expresses a total monetary cost of operating the central plant over the optimization period as a function of the utility rate data and an amount of the one or more resources consumed by multiple groups of the central plant equipment. The optimization system includes a load change penalty module configured to modify the objective function to account for a load change penalty resulting from a change in an amount of the building energy loads assigned to one or more of the groups of central plant equipment.

    Abstract translation: 用于中央设备的优化系统包括处理电路，其被配置为接收指示建筑物能量负载的负荷预测数据和表示中央设备的设备消耗的一个或多个资源的价格的效用率数据，以用于建筑物能量负荷。 所述优化系统包括高级优化模块，其被配置为生成目标函数，所述目标函数表示在所述优化周期内操作所述中心工厂的总货币成本，作为所述效用率数据的函数以及多个所消耗的所述一个或多个资源的量 集团的中央设备设备。 优化系统包括负载变化罚款模块，其被配置为修改目标函数以解决由分配给一组或多组中央工厂设备的建筑物能量负荷的量的变化导致的负荷变化损失。

    Model predictive maintenance system with degradation impact model
    24.
    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
    26.
    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
    30.
    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.

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