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    Non-intrusive load monitoring using machine learning
    2.
    发明授权
    Non-intrusive load monitoring using machine learning 有权

    公开(公告)号：US11593645B2

    公开(公告)日：2023-02-28

    申请号：US16698333

    申请日：2019-11-27

    Applicant: Oracle International Corporation

    Inventor： Selim Mimaroglu ,  Oren Benjamin ,  Arhan Gunel ,  Anqi Shen

    IPC: G06N3/08 ,  G06N3/04

    Abstract: Embodiments implement non-intrusive load monitoring using machine learning. A trained convolutional neural network (CNN) can be stored, where the CNN includes a plurality of layers, and the CNN is trained to predict disaggregated target device energy usage data from within source location energy usage data based on training data including labeled energy usage data from a plurality of source locations. Input data can be received including energy usage data at a source location over a period of time. Disaggregated target device energy usage can be predicted, using the trained CNN, based on the input data.

    Non-intrusive load monitoring using ensemble machine learning techniques
    3.
    发明授权
    Non-intrusive load monitoring using ensemble machine learning techniques 有权

    公开(公告)号：US11544632B2

    公开(公告)日：2023-01-03

    申请号：US16698338

    申请日：2019-11-27

    Applicant: Oracle International Corporation

    Inventor： Selim Mimaroglu ,  Anqi Shen ,  Arhan Gunel ,  Oren Benjamin

    IPC: G06N20/20 ,  H02J3/00 ,  H02J13/00 ,  G06N5/04

    Abstract: Embodiments implement non-intrusive load monitoring using ensemble machine learning techniques. A first trained machine learning model configured to disaggregate target device energy usage from source location energy usage and a second trained machine learning model configured to detect device energy usage from source location energy usage can be stored, where the first trained machine learning model is trained to predict an amount of energy usage for the target device and the second trained machine learning model is trained to predict when a target device has used energy. Source location energy usage over a period of time can be received, where the source location energy usage includes energy consumed by the target device. An amount of disaggregated target device energy usage over the period of time can be predicted, using the first and second trained machine learning models, based on the received source location energy usage.

    Non-intrusive load monitoring using machine learning and processed training data
    5.
    发明授权
    Non-intrusive load monitoring using machine learning and processed training data 有权

    公开(公告)号：US11636356B2

    公开(公告)日：2023-04-25

    申请号：US16698346

    申请日：2019-11-27

    Applicant: Oracle International Corporation

    Inventor： Selim Mimaroglu ,  Arhan Gunel ,  Oren Benjamin ,  Anqi Shen

    IPC: G06N5/04 ,  G06N20/00 ,  G06Q50/06

    Abstract: Embodiments implement non-intrusive load monitoring using a novel learning scheme. A trained machine learning model configured to disaggregate device energy usage from household energy usage can be stored, where the machine learning model is trained to predict energy usage for a target device from household energy usage. Household energy usage over a period of time can be received, where the household energy usage includes energy consumed by the target device and energy consumed by a plurality of other devices. Using the trained machine learning model, energy usage for the target device over the period of time can be predicted based on the received household energy usage.

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