一种基于度量学习的自适应非侵入式负荷识别方法

An adaptive non-intrusive load identification method based on metric learning

现有非侵入式负荷识别技术大多基于最优化和模式识别算法,两种算法在模型泛化能力和未知负荷识别上均存在一定缺陷。针对这一问题,文中提出一种基于度量学习的非侵入式负荷识别模型,通过卷积神经网络将负荷电流特性映射到度量空间,在网络训练时使用三元组损失实现特征的集聚,对度量空间特征进行相似度判别实现负荷辨识。所提方法可实现对未知负荷的有效识别,并具有较强的泛化能力;另一方面,度量学习作为小样本学习的方法之一,能够减轻对训练样本的依赖,具有较高的实用性。

Most of the existing non-intrusive load identification technologies are based on optimization and pattern recognition algorithms.Both algorithms have certain defects in model generalization ability and unknown load identification.In response to this problem,this paper proposes a non-intrusive load identification model based on metric learning.The load current characteristics are mapped into the metric space through a convolutional neural network,and the triplet loss is used during network training for feature aggregation.Then,the load identification is realized by similarity discrimination of metric space features,which can realize effective recognition of unknown loads,and has strong generalization ability.On the other hand,metric learning is one of the methods of few-shot learning,which can reduce the dependence on training samples and has high practicability.