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Deep Learning Detection of Inaccurate Smart Electricity Meters: A Case Study
IEEE Transactions on Neural Networks and Learning Systems ( IF 10.4 ) Pub Date : 2020-12-21 , DOI: 10.1109/mie.2020.3026197
Ming Liu , Dongpeng Liu , Guangyu Sun , Yi Zhao , Duolin Wang , Fangxing Liu , Xiang Fang , Qing He , Dong Xu

Detecting inaccurate smart meters and targeting them for replacement can save significant resources. For this purpose, a novel deeplearning method was developed based on long short-term memory (LSTM) and a modified convolutional neural network (CNN) to predict electricity usage trajectories based on historical data. From the significant difference between the predicted trajectory and the observed one, the meters that cannot measure electricity accurately are located. In a case study, a proof of principle is demonstrated for detecting inaccurate meters with high accuracy for practical usage to prevent unnecessary replacement and increase the service lifespan of smart meters.

中文翻译:

不准确的智能电表的深度学习检测:一个案例研究

检测不准确的智能电表并将其定位为更换目标可以节省大量资源。为此,开发了一种基于长短期记忆(LSTM)和改进的卷积神经网络(CNN)的新型深度学习方法,以根据历史数据预测用电轨迹。根据预测的轨迹与观测到的轨迹之间的显着差异,找到无法准确测量电的电表。在一个案例研究中,演示了一种原理证明,可以在实际使用中以高精度检测不准确的电表,以防止不必要的更换并延长智能电表的使用寿命。
更新日期:2020-12-22
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