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Non-invasive load monitoring method and device based on semi-supervised learning algorithm

A semi-supervised learning, non-invasive technology, applied in the field of non-invasive load monitoring based on semi-supervised learning algorithm, can solve the problem that the ILM method cannot be widely used, and achieve the effect of simplifying the prediction problem

Active Publication Date: 2020-10-02
TONGJI UNIV
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Problems solved by technology

ILM methods cannot be widely adopted due to privacy and cost concerns

Method used

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  • Non-invasive load monitoring method and device based on semi-supervised learning algorithm
  • Non-invasive load monitoring method and device based on semi-supervised learning algorithm
  • Non-invasive load monitoring method and device based on semi-supervised learning algorithm

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Embodiment Construction

[0040] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is carried out on the premise of the technical solution of the present invention, and detailed implementation and specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

[0041] A non-intrusive load monitoring method based on a semi-supervised learning algorithm, the method is implemented by a computer system in the form of a computer program, and the device includes a memory, a processor, and a program stored in the memory and executed by the processor, such as figure 1 As shown, the processor implements the following steps when executing the program:

[0042] Step S1: Collect the time series information of the total power consumption of the smart meter and the operating status information of each device;

[0043] The goal of load decomposition is to...

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Abstract

The present invention relates to a non-intrusive load monitoring method and device based on a semi-supervised learning algorithm, wherein the method includes: step S1: collecting the time series information of the total power consumption of the smart meter and the operating status information of each device; step S2, preprocessing the data , first clean the data, then normalize the data, and finally fill the beginning and end of the total power consumption sequence with 0; step S3, slide a time length each time to obtain the training window, and use the total power consumption sequence as the input window data, Take the switch state of the device at the midpoint of the sequence as the output label, and repeat it multiple times to obtain the training sample data set; step S4, use the training sample to train the neural network model; step S5, input the total power sequence to be identified into the trained neural network model , you can get the correct running status of each device. Compared with the prior art, the present invention has the advantages of being able to obtain refined user internal device usage status.

Description

technical field [0001] The invention relates to a load monitoring method, in particular to a non-invasive load monitoring method and device based on a semi-supervised learning algorithm. Background technique [0002] Currently, energy conservation is an extremely challenging issue as energy demand is increasing exponentially. Many researchers are trying to find effective ways to solve this problem. In China, residential electricity consumption accounts for about 13.04% of the total electricity consumption (about 756 billion kWh / year). Therefore, the residential energy saving part will have a major impact on the overall energy saving and consumption reduction. Many researchers believe that real-time feedback is a very useful mechanism, but the current electricity metering and billing infrastructure cannot solve this problem. We need to monitor real-time consumption of devices and provide consumers with real-time actionable feedback. Through this feedback, consumers can kn...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01R31/00G01R21/00G06K9/62G06N3/04G06N3/08
CPCG01R31/00G01R21/001G01R21/002G06N3/08G06N3/045G06F18/2155
Inventor 赵生捷缪楠张荣庆
Owner TONGJI UNIV
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