基于深度循环卷积模型的非侵入式负荷分解方法

A non-invasive load decomposition method based on deep circular convolutional model

电力分项计算是智能电能表的一个重要环节,即对接入户线的各个电器设备进行用电消耗检测。对电力公司进行精准预测,提高系统稳定性可靠性,制定调度方案,设计“错峰用电”费率结构,发现设备老化和故障有着重要意义。为了实现电力分项计算,文中提出了一种基于深度循环卷积神经网络的非侵入式负荷分解方法。对目标电器的不同功率状态进行编码,用循环卷积神经网络提取输入负荷总功率的空间时间特征。对输入数据进行归一化提高模型训练速度,用drouput技术降低模型过拟合,用迁移学习技术实现对不同目标电器的功率状态预测建模。并和传统的隐马尔可夫模型进行对比。采用公开的redd数据集,结果证明文中所提出的模型能很好预测目标电器的功率状态。

The itemized calculation of electricity is an important part of the smart meter,which is to test the electricity consumption of each appliance in the entry line.It is of great significance to accurately predict the power companies,improve the stability and reliability of the system,formulate scheduling plans,design the rate structure of“off-peak electricity”,and discover the aging and failure of equipment.A non-invasive load decomposition method based on deep circular convolutional neural network is proposed in this paper.Different power states of target electrical appliances are coded and the spatial and temporal characteristics of the total power of input load are extracted by circular convolutional neural network.The normalization of input data improves the speed of model training,the drouput technology is adopted to reduce model fitting,and the transfer learning technology is adopted to realize the power state prediction modeling of different target electrical appliances.Compared with the traditional hidden Markov model,the results show that the model proposed in this paper can predict the power state of the target electrical appliance well.