Non-intrusive residential load disaggregation model based on fully convolutional denoising auto-encoder and convolutional block attention module

被引：0
|
作者：
Lin S. [1 ]
Li Y. [1 ]
Shen Y. [1 ]
Lin Y. [1 ]
Li D. [1 ]
机构：
[1] School of Electrical Engineering, Shanghai University of Electric Power, Shanghai
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2024年 / 44卷 / 03期
基金：
中国国家自然科学基金;
关键词：
attention module; fully convolutional denoising auto-encoder; gating unit; load disaggregation; subtask network;
D O I：
10.16081/j.epae.202306004
中图分类号：
学科分类号：
摘要：
In order to further improve the disaggregation accuracy and generalization ability of low-frequency residential load disaggregation model，a non-intrusive residential load disaggregation model based on fully convolutional denoising auto-encoder and convolutional block attention module is proposed，which can deeply analyze the power curve of a single appliance. The main regression subtask network and auxiliary classification subtask network are respectively constructed based on fully convolutional denoising auto-encoder. In the subtask network，the convolutional block attention module is introduced to adaptively assign the feature attention weight，which reduces the influence of unimportant factors in the model training process. The output of auxiliary classification subtask network is taken as the gating unit of the output of main regression subtask network，and the final load decomposition is realized. The example results based on public datasets show that the proposed load disaggregation model has better disaggregation accuracy and generalization ability than the existing load disaggregation models. © 2024 Electric Power Automation Equipment Press. All rights reserved.
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页码：127 / 133
页数：6
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