Load monitoring using machine learning

A processor, energy technology for utility metering equipment that solves problems such as the difficulty of successful decomposition and limited availability hindering progress

Pending Publication Date: 2021-11-05
ORACLE INT CORP
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Advances in metering equipment have provided some opportunities, but successful disaggregation remains elusive
The limited availability of labeled datasets or source location energy usage values ​​with labeled appliance energy usage values ​​(e.g., household energy usage values ​​tagged with energy usage values ​​of appliance 1, electric vehicle 1, appliance 2, etc.) has further impeded progress

Method used

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  • Load monitoring using machine learning
  • Load monitoring using machine learning
  • Load monitoring using machine learning

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Embodiment Construction

[0017] Embodiments perform non-intrusive load monitoring using a novel learning scheme. NILM and decomposition refer to taking total energy use at a source location (e.g., energy use at a household provided by an advanced metering infrastructure) as input and estimating one or more appliances, electric vehicles, and Energy usage of other devices for energy. Embodiments utilize trained machine learning models to predict the energy usage of target devices based on the overall energy usage at the source location. For example, the target device could be a white goods or electric vehicle, the source location could be a home, and the trained machine learning model could receive as input the energy usage of the home and predict the energy usage of the target device (e.g., included in the overall Energy usage of the target device in Energy usage of the home).

[0018] Embodiments use labeled energy usage data to train a machine learning model. For example, a machine learning model,...

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PUM

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Abstract

Embodiments implement non-intrusive load monitoring using machine learning. A trained convolutional neural network (CNN) can be stored, wherein 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.

Description

technical field [0001] Embodiments of the present disclosure relate generally to utility metering, and more particularly to non-intrusive load monitoring using utility metering. Background technique [0002] Non-intrusive load monitoring ("NILM") and disaggregation of various energy-using devices at a given source location have proven challenging. For example, given a household, disaggregating equipment and / or electric vehicle energy usage from within the household's overall monitored energy usage is difficult, in part because of the wide variety of household equipment and / or electric vehicles (e.g., make, model, year Wait). Advances in metering equipment have provided some opportunities, but successful decomposition remains elusive. The limited availability of labeled datasets or source location energy usage values ​​with labeled appliance energy usage values ​​(e.g., household energy usage values ​​tagged with energy usage values ​​of appliance 1, electric vehicle 1, app...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N20/00G06N3/04G06N3/08G06K9/62
CPCG06N20/00G06N3/08G06N3/045G06F18/214H02J3/003H02J2203/20G06N20/10Y04S10/50Y04S40/20Y02E60/00H02J2310/70G06N7/01G06N3/044G06N3/04
Inventor S·米玛洛格鲁O·本杰明A·刚奈尔A·沈
Owner ORACLE INT CORP
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