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    PREDICTIVELY CONTROLLING AN ENVIRONMENTAL CONTROL SYSTEM
    1.
    发明申请
    PREDICTIVELY CONTROLLING AN ENVIRONMENTAL CONTROL SYSTEM 审中-公开
    预测环境控制系统

    公开(公告)号：US20160201933A1

    公开(公告)日：2016-07-14

    申请号：US14597196

    申请日：2015-01-14

    Applicant: Google Inc.

    Inventor： Todd Andrew Hester ,  Evan Jarman Fisher ,  Piyush Khandelwal

    IPC: F24F11/00 ,  G05B15/02 ,  F24D19/10

    CPC classification number: G05B15/02 ,  F24D19/1084 ,  F24F11/30 ,  F24F11/46 ,  F24F11/47 ,  F24F11/56 ,  F24F11/62 ,  F24F11/64 ,  F24F11/65 ,  F24F11/70 ,  F24F2110/10 ,  F24F2110/12 ,  F24F2120/10 ,  F24F2130/00 ,  F24F2130/10 ,  F24F2130/20 ,  F24F2140/60

    Abstract: In an embodiment, an electronic device may include a power source configured to provide operational power to the electronic device and a processor coupled to the power source. The processor may be configured to generate temperature predictions using a model of a structure and possible control scenarios, determine a value of the temperature predictions and the respective possible control scenarios using a cost function, the cost function comprising weighted factors related to an error between a setpoint temperature and the temperature predictions, a length of runtime for an environmental control system (e.g., an HVAC system), and a length of environmental control system cycles. The processor may also be configured to select the control scenario with the highest value to apply to control the environmental control system. The control scenarios may be generated using upper confidence bound for trees (UCT).

    Abstract translation: 在一个实施例中，电子设备可以包括被配置为向电子设备提供操作功率的电源和耦合到电源的处理器。 处理器可以被配置为使用结构模型和可能的控制场景来生成温度预测，使用成本函数确定温度预测值和各个可能的控制场景的成本函数，成本函数包括与 设定点温度和温度预测，环境控制系统（例如HVAC系统）的运行时间以及环境控制系统周期的长度。 处理器还可以被配置为选择具有最高值的控制场景来应用于控制环境控制系统。 可以使用树的高置信界限（UCT）来生成控制场景。

    OPTIMIZING DATA CENTER CONTROLS USING NEURAL NETWORKS
    2.
    发明申请
    OPTIMIZING DATA CENTER CONTROLS USING NEURAL NETWORKS 审中-公开

    公开(公告)号：US20180204116A1

    公开(公告)日：2018-07-19

    申请号：US15410547

    申请日：2017-01-19

    Applicant: Google Inc.

    Inventor： Richard Andrew Evans ,  Jim Gao ,  Michael C. Ryan ,  Gabriel Dulac-Arnold ,  Jonathan Karl Scholz ,  Todd Andrew Hester

    IPC: G06N3/08 ,  G06N3/04

    CPC classification number: G06N3/0454

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for improving operational efficiency within a data center by modeling data center performance and predicting power usage efficiency. An example method receives a state input characterizing a current state of a data center. For each data center setting slate, the state input and the data center setting slate are processed through an ensemble of machine learning models. Each machine learning model is configured to receive and process the state input and the data center setting slate to generate an efficiency score that characterizes a predicted resource efficiency of the data center if the data center settings defined by the data center setting slate are adopted t. The method selects, based on the efficiency scores for the data center setting slates, new values for the data center settings.

    PREDICTIVELY CONTROLLING AN ENVIRONMENTAL CONTROL SYSTEM USING UPPER CONFIDENCE BOUND FOR TREES
    5.
    发明申请
    PREDICTIVELY CONTROLLING AN ENVIRONMENTAL CONTROL SYSTEM USING UPPER CONFIDENCE BOUND FOR TREES 有权
    预防性控制环境控制系统

    公开(公告)号：US20160201934A1

    公开(公告)日：2016-07-14

    申请号：US14597198

    申请日：2015-01-14

    Applicant: Google Inc.

    Inventor： Todd Andrew Hester ,  Evan Jarman Fisher ,  Piyush Khandelwal

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

    CPC classification number: F24F11/62 ,  F24F11/30 ,  F24F11/46 ,  F24F11/47 ,  F24F11/56 ,  F24F11/63 ,  F24F11/64 ,  F24F11/65 ,  F24F11/70 ,  F24F11/89 ,  F24F2110/10 ,  F24F2130/00 ,  F24F2130/10 ,  F24F2130/20 ,  F24F2140/50 ,  F24F2140/60 ,  G05B17/02

    Abstract: In an embodiment, an electronic device may include a processor that may iteratively simulate candidate control trajectories using upper confidence bound for trees (UCT) to control an environmental control system (e.g., an HVAC system). Each candidate control trajectory may be simulated by selecting a control action at each of a plurality of time steps over a period of time that has the highest upper bound on possible performance using values from previous simulations and predicting a temperature for a next time step of the plurality of time steps that results from applying the selected control action using a thermal model. The processor may determine a value of each candidate control trajectory using a cost function, update the value of each control action selected in each candidate control trajectory, and select a candidate control trajectory with the highest value using UCT to apply to control the environmental control system.

    Abstract translation: 在一个实施例中，电子设备可以包括处理器，其可以使用树的高置信界（UCT）来迭代地模拟候选控制轨迹，以控制环境控制系统（例如HVAC系统）。 每个候选控制轨迹可以通过在多个时间步长中的每个时间段上选择一个控制动作，该控制动作在具有最高上限的可能性能上使用来自先前模拟的值，并且预测下一个时间步长的温度 使用热模型应用所选择的控制动作产生的多个时间步长。 处理器可以使用成本函数确定每个候选控制轨迹的值，更新在每个候选控制轨迹中选择的每个控制动作的值，并且使用UCT选择具有最高值的候选控制轨迹以应用于控制环境控制系统 。

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