基于深度循环神经网络的异常用电检测方法

Abnormal electricity detection method based on deep recurrent neural network

现有的窃电检测方法通常利用电力用户的静态特征和浅层的检测模型,没有充分利用隐含在数据下的时序特征。为此,该文提出基于双向深度循环神经网络的窃电检测方法,分别采用门控循环单元和长短时记忆网络建立双向深度循环神经网络模型,输入用户的用电量数据,利用循环神经网络提取数据的时序特性,将时序特征输入反向传播神经网络进行分类。对爱尔兰社会科学数据档案馆提供的电力用户行为试验数据进行实验分析,该数据集包含5000个家庭和企业用户超过一年的用电量数据,采样时间为30 min。结果表明,与传统浅层的神经网络模型相比,双向深度循环神经网络的窃电检测方法具有更高的准确度和鲁棒性。

Existing electricity theft detection methods usually use the static characteristics of power users and shallow detection models,and do not make full use of the timing characteristics hidden in the data.To this end,this paper proposes an electricity theft detection method based on a bidirectional deep recurrent neural network,which uses a gated recurrent unit and a long-short-term memory network to establish a bidirectional deep recurrent neural network model,input user power consumption data,and use the recurrent neural network to extract data time series characteristics,input the time series characteristics into the back propagation neural network for classification.Conducted an experimental analysis of the power user behavior test data provided by the Irish Social Science Data Archive.The data set contains electricity consumption data for 5000 households and business users for more than one year,and the sampling time is 30 minutes.The results show that,compared with the traditional shallow neural network model,the bidirectional deep recurrent neural network detection method has higher accuracy and robustness.