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：

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不准确智能电表的深度学习检测：案例研究

检测不准确的智能电表并针对它们进行更换可以节省大量资源。为此，基于长短期记忆 (LSTM) 和改进的卷积神经网络 (CNN) 开发了一种新的深度学习方法，以根据历史数据预测用电轨迹。从预测轨迹与观测轨迹的显着差异来看，无法准确测量电量的仪表被定位。在一个案例研究中，展示了一种原理证明，用于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。