采用多尺度注意力机制的非侵入式居民负荷分解方法

Non-Intrusive Load Decomposition Method of Residential Customer by Multi-Scale Attention Mechanism

非侵入式负荷监测是客户侧智慧物联场景的重要技术之一。为提高非侵入式负荷分解辨识的准确性,文章提出了一种多尺度注意力机制与卷积神经网络组合的居民负荷分解方法。首先,将注意力模型前几个时刻正常的负荷数据注意力得分对当前时刻可能存在异常得分进行动态平滑,通过引入约束因子对负荷辨识注意力模型进行优化。然后,采用尺寸不同的卷积滤波器对多种电器设备混合叠加的负荷数据进行建模,以挖掘更丰富的负荷特征信息。最后以PLAID数据集为例,将所提方法与较为流行的负荷分解方法进行对比。实验结果表明,文章的基于多尺度注意力机制的方法,负荷分解效果有较大提升,降低了负荷特征相近的电器辨识混淆问题。

Non-intrusive load monitoring is one of the important technologies in the customer-side smart IoT scenario.In order to improve the accuracy of non-intrusive load decomposition and identification,this paper proposes a residential customer load decomposition method combining multi-scale attention mechanism and convolutional neural network.Firstly,the attention scores of the normal load data at the previous few moments of the attention model are smoothed dynamically against the abnormal scores at the current moment.The load identification attention model is optimized by constraint factors.Then,on this basis,convolution filters of different sizes are used to model the mixed load data of different electrical equipment,to mine more abundant characteristic information.Finally,taking the PLAID data set as an example,this paper compares the proposed method with the more popular load decomposition method.The experimental results show that the method based on the multi-scale attention mechanism in this paper can greatly improve the effect of load decomposition and reduce the confusion problem of electrical appliance identification with similar load characteristics.