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Load decomposition method based on electrical appliance physical characteristics and residential electricity consumption behaviors

A technology of residential electricity consumption and physical characteristics, applied in data processing applications, instruments, character and pattern recognition, etc., can solve the problems of missing original data, insufficient consideration of electrical physical characteristics and residential electricity consumption behaviors, etc., to improve network accuracy, Make up for the effect of insufficient data volume

Active Publication Date: 2020-08-21
HANGZHOU DIANZI UNIV
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Problems solved by technology

[0003] Aiming at the lack of original data measured by smart meters and the lack of consideration of the physical characteristics of electrical appliances and residents' electricity consumption behavior in existing non-invasive load decomposition methods, the present invention proposes a method based on the physical characteristics of electrical appliances and residents' electricity consumption behavior. combined non-intrusive load decomposition method

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  • Load decomposition method based on electrical appliance physical characteristics and residential electricity consumption behaviors
  • Load decomposition method based on electrical appliance physical characteristics and residential electricity consumption behaviors
  • Load decomposition method based on electrical appliance physical characteristics and residential electricity consumption behaviors

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Embodiment Construction

[0049] The present invention will be described in detail below in combination with specific embodiments.

[0050] Such as figure 1 As shown, the improvement proposed by the present invention is implemented according to the following steps based on deep learning non-intrusive load decomposition:

[0051] Step 1: First, check whether there are missing values ​​in the historical operating power data of each household appliance obtained according to the sampling frequency, then analyze the abnormal data of the obtained power by using the box plot, and process the abnormal data by using the binning method. The specific method is as follows:

[0052] 1) The operating power data of each household appliance generally obeys the Gaussian distribution, so the operating power data is sorted from large to small, and the power at the upper quartile is defined as U (U is at the 25% position in the order from large to small), and the lower quartile is The power at the position is L (arrangi...

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Abstract

The invention discloses a load decomposition method based on electrical appliance physical characteristics and residential electricity consumption behaviors. In order to reduce the influence of original data on load feature extraction, the method comprises firstly, using a binning method to perform noise reduction processing on historical operation data of household appliances, and meanwhile, dividing the household appliances into intermittent household appliances and continuous household appliances according to the operation conditions of the household appliances; extracting fundamental waves, third harmonic waves, fifth harmonic waves and seventh harmonic waves of the current as load characteristics of the intermittent household electrical appliances and extracting operating power as load characteristics of the continuous household electrical appliances, separately establishing a deep neural network and an MLP neural network to train the intermittent household electrical appliances and the continuous household electrical appliances, and realize load decomposition of different types of household electrical appliances.

Description

technical field [0001] The invention relates to the field of load decomposition of non-invasive load monitoring, in particular to a non-invasive load decomposition method based on the combination of physical characteristics of electrical appliances and residents' electricity consumption behavior. Background technique [0002] The existing non-intrusive load decomposition method mainly establishes the corresponding electrical load model based on the electrical characteristics of the load measured by the smart meter, and then uses the pattern recognition technology to realize the decomposition of the electric load. When smart meters collect data, it is inevitable that there will be missing data. If the original data is directly used to extract the load characteristics, it will definitely affect the load decomposition. Therefore, it is essential to deal with the abnormal data in the original collected data. In addition, due to the different physical characteristics of electrica...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q50/06G06K9/62G06F30/27
CPCG06Q50/06G06F30/27G06F18/23213
Inventor 罗平樊星驰徐平邱富康虞俊锋
Owner HANGZHOU DIANZI UNIV
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