基于联合神经网络的用户窃电行为识别模型

Recognition Model of User Electricity Stealing Behavior Based on Joint Neural Network

针对窃电行为识别准确率低的问题,提出了基于联合神经网络的窃电行为识别模型。首先,对获取的用户用电数据进行处理,利用格拉姆角场方法对用户用电数据进行二维化处理。然后,针对不同维度的用电数据,提出了基于联合神经网络的用户窃电行为识别模型,利用卷积神经网络(CNN)和双向长短期记忆(BiLSTM)神经网络提取一维用电数据和二维用电数据特征。通过实例分析表明,提出的联合神经网络模型对窃电行为识别准确率达到90%以上,证明所建立的评估模型为解决窃电问题提供了一种切实可行的方案。

Aiming at the problem of low recognition accuracy of electricity stealing behavior,an electricity stealing behavior recognition model based on joint neural network was proposed.Firstly,the acquired user electricity consumption data was processed,and the user electricity consumption data was two-dimensionally processed by using the Gramian angular field method.Then,for the electricity consumption data of different dimensions,a user electricity stealing behavior recognition model based on the joint neural network was proposed,and the features of the one-dimensional electricity consumption data and the two-dimensional electricity consumption data were extracted by using the convolutional neural network(CNN)and the bidirectional long shortterm memory(BiLSTM)neural network.The case analysis shows that the proposed joint neural network model has an accuracy rate of more than 90% for the recognition of electricity stealing behavior,which proves that the established evaluation model provides a practical solution to the electricity stealing problem.