Accurate Identification of Electrical Equipment from Power Load Profiles

摘要：

　　It is essential for the power industries to identify the running electrical equipment automatically.For power monitoring,the load profile data vary with the equipments types.Proceeding from the fundamental features of load time series,we propose a method to identify electrical equipment from power load profiles accurately.Aiming to improve the classification accuracy and generalization performance of convolutional neural network(CNN),we combine the training process of generative adversarial networks(GANs)with CNN,which employs the generated samples to enhance the classification accuracy.The CNN and discriminator in our approach share the first convolution layer for extracting richer features.We evaluate our method on UCR data sets comparing with 12 existing methods.Furthermore,we compare our model with LSTM,GRU and CNN on the electrical equipment load data,which is from industries in certain area.The final results show that our model has a higher equipment identification accuracy than other deep learning models.

关键词： Power load profiles CNN GAN Time series

作者: Ziyi Wang Chun Li Lin Shang

作者单位: National Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210023,China

会议类型: 国际会议

会议名称: The 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (第23届亚太知识发现和数据挖掘国际会议(PAKDD2019)

会议地点: 澳门

会议语种:英文

页码: 43-55

在线出版日期: 2019-04-14（万方平台首次上网日期，不代表论文的发表时间）