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基于多注意力机制集成的非侵入式负荷分解算法
作者:
作者单位:

1.上海海事大学;2.南京信息工程大学;3.同济大学;4.广东海洋大学

基金项目:

国家自然科学青年基金


Non-intrusive Load Decomposition Model Based on Multi-attention Mechanism Integration
Author:
Affiliation:

1.Shanghai Maritime University;2.Nanjing University of Information Engineering;3.Tongji University;4.Guangdong Ocean University

Fund Project:

National Natural Science Youth Foundation of China

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    摘要:

    针对输入负荷特征对分解结果的重要程度不同，以及长短时记忆网络（LSTM）在捕捉长时间用电信息的时间依赖性方面受限导致分解误差高的问题，提出一种基于多注意力机制集成的非侵入式负荷分解算法。首先，利用概率自注意力机制对一维空洞卷积提取到的负荷特征进行优化处理，实现重要负荷特征的遴选；其次，采用时间模式注意力机制对LSTM的隐状态赋予权重，从而增强网络对长时间用电信息之间的时间依赖性的学习能力；最后，利用公开数据集UKDALE和REDD对所提分解模型的有效性和创新性进行验证。实验结果表明，与其他多种现有分解算法相比，基于多注意力机制集成的分解算法不仅具备更好的负荷特征遴选能力，而且能正确建立特征之间的时间依赖关系，有效降低了分解误差。

    Abstract:

    In view of the different importance of input load characteristics to the decomposition results and the high decomposition error caused by the limited time dependence of LSTM in capturing long-term power consumption information, a non-intrusive load decomposition model based on multi-attention mechanism integration is proposed. Firstly, the probsparse self-attention mechanism is used to optimize the load characteristics extracted by one-dimensional dilated convolution. Then, the temporal pattern attention mechanism is used to give weight to the hidden state of LSTM, so as to enhance the learning ability of the network on the time dependence of long-term power consumption information. Finally, the validity of the proposed decomposition model is verified using the publicly available dataset UKDALE and REDD. Experimental results show that, compared with other decomposition algorithms, the decomposition algorithm based on multi-attention mechanism integration not only has the ability to select important load features, but also can correctly establish the time-dependent relationship between features and effectively reduce the decomposition error.

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王赟,葛泉波,姚刚,王梦梦,姜淏予.基于多注意力机制集成的非侵入式负荷分解算法[J].南京信息工程大学学报,,():

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  • 收稿日期:2022-05-16
  • 最后修改日期:2022-06-14
  • 录用日期:2022-06-15

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