面向家庭用电负荷分解的时间卷积注意力网络

Temporal convolutional attention-based networkfor household electric load disaggregation

针对传统深度神经网络分解模型准确度不能满足非侵入式负荷监测实际需求的现状,提出了一种基于时间卷积网络和注意力机制的负荷分解网络(TCNA)。采用序列到点的分解方法,使用改进的时间卷积网络为基础提取负荷数据特征,增加卷积核感受野,获取更多数据特征信息。模型结合注意力模块,提取到更加丰富和有价值的特征信息,提升了训练效率。在UK-dale数据集上的实验结果表明:该模型比现有的分解方法在分解性能和电器启停状态判断方面有明显提升。

Aiming at the fact that the accuracy of the traditional deep neural network disaggregation model still cannot meet the actual needs of non-invasive load monitoring,this paper proposes a load disaggregation model based on Temporal Convolutional Attention-based Network(TCAN).The model adopts the sequence-to-point disaggregation method,uses the improved temporal convolutional network as the basis to extract load data characteristics,increases the convolutional kernel sensing field,and obtains more data feature information.The model combines the attention module to extract richer and more valuable feature information,which improves the training efficiency.The experimental results in the UK-dale dataset show that the model has significant improvement in decomposing performance and judging the start-stop state of electrical appliances than the existing disaggregation methods.