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Non-intrusive load monitoring using ensemble machine learning techniques

A machine learning model and usage technology, applied in the direction of ensemble learning, load forecasting in communication networks, neural learning methods, etc., can solve the problems that successful decomposition is difficult to achieve, limited availability hinders progress, etc.

Pending Publication Date: 2021-10-29
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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  • Non-intrusive load monitoring using ensemble machine learning techniques
  • Non-intrusive load monitoring using ensemble machine learning techniques
  • Non-intrusive load monitoring using ensemble machine learning techniques

Examples

Experimental program
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Effect test

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 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.

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): G06N20/20G06N3/04G06N3/08G06K9/62
CPCG06N20/20G06N3/08G06N3/045G06F18/214G06Q10/04G06Q50/06H02J3/003H02J2203/20H02J13/00002G06N5/04
Inventor S·米玛洛格鲁A·沈A·刚奈尔O·本杰明
Owner ORACLE INT CORP