基于注意力时序网络模型的非侵入式负荷分解
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作者单位:

1.国网河南电动汽车服务有限公司;2.南京信息工程大学

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中图分类号:

TP

基金项目:

国家自然科学基金


Non-Intrusive Load Decomposition Based on Attention Recurrent Network Model
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Affiliation:

1.State grid henan electric vehicle service company;2.Nanjing University of Information Science and Technology

Fund Project:

National Natural Science Foundation of PR China

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    摘要:

    非侵入式负荷分解的本质是根据已知的总功率信号分解出单一的负荷设备的功率信号。目前基于深度学习模型大都存在网络模型负荷特征提取不充分、分解精度低、对使用频率较低的负荷设备分解误差大等问题。针对这些问题,本文提出了一种注意力时序网络模型(Attention Recurrent Neural Networks, ARNN)来实现非侵入式负荷分解,它将回归网络与分类网络相结合来解决非侵入式负荷分解问题。该模型通过RNN网络实现对序列信号特征的提取,同时利用注意力机制定位输入序列中重要信息的位置,提高神经网络的表征能力。在公开数据集Wiki-Energy以及UK-DALE上进行的实验表明,我们提出的深度神经网络在所有考虑的实验条件下都优于最先进的神经网络。我们还表明,通过注意力机制和辅助分类网络能够正确检测设备的开启或关闭,并定位高功耗的信号部分,提高了负荷分解的准确性。

    Abstract:

    The non-intrusive load decomposition is to decompose the power signal of a single load device according to the known total power signal. At present, there are many problems based on deep learning models, such as insufficient load feature extraction, low decomposition accuracy, large decomposition error of load equipment with low frequency, and so on. To solve these problems, this paper proposes an attention recurrent neural networks (ARNN) model to realize non-invasive load decomposition, which combines regression network and classification network to solve the non-invasive load decomposition problem. The model extracts the features of sequence signals through RNN network, and uses the attention mechanism to locate the position of important information in the input sequence, so as to improve the representation ability of network. Experiments on public datasets Wiki-Energy and UK-DALE show that our proposed deep neural network is superior to the most advanced neural network under all experimental conditions. We also show that the attention mechanism and auxiliary classification network can correctly detect the opening or closing of devices, and locate the high-power signal part, which improves the accuracy of load decomposition.

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历史
  • 收稿日期:2022-07-03
  • 最后修改日期:2022-08-14
  • 录用日期:2022-08-18
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