Detection of Malfunctioning Smart Electricity Meter

Detecting malfunctional smart meters based on electricity usage and targeting them for replacement can save significant resources. For this purpose, we developed a novel deep-learning method for malfunctional smart meter detection based on long short-term memory (LSTM) and a modified convolutional neural network (CNN). Our method uses LSTM to predict the reading of a master meter based on data collected from submeters. If the predicted value is significantly different from master meter reading data over a period of time, the diagnosis part will be activated, classifying every submeter to identify the malfunctional submeter based on CNN. We propose a time series-recurrence plot (TS-RP) CNN, by combining the sequential raw data of electricity and its recurrence plots in the phase space as dual input branches of CNN. By combining this time sequential (TS) raw data with the recurrence plots (RP), we found that the classification performance was much better than when using the sequential raw data only. We compared our method with several classical methods, including the elastic net and gradient boosting regression methods, which show that our method performs better. To the best of our knowledge, our TS-RP CNN is the first method to apply deep learning in malfunctional meter detection. It is also relatively unique in the way it combines sequential data and its phase-space transformation as the dual input for general sequential data classification. This method is not only useful for increasing the service life span of smart meters, preventing unnecessary replacement, but it also provides a general method for managing other instruments of sequential data.