Detection of Malfunctioning Smart Electricity Meter

07/26/2019
by   Ming Liu, et al.
0

In this paper, a method for malfunctioning smart meter detection, based on Long Short-Term Memory (LSTM) and Temporal Phase Convolutional Neural Network (TPCNN), is proposed originally. This method is very useful for some developing countries where smart meters have not been popularized but in high demand. In addition, it is a new topic that people try to increase the service life span of smart meters to prevent unnecessary waste by detecting malfunctioning meters. We are the first people complete a combination of malfunctioning meters detection and prediction model based on deep learning methods. To the best our knowledge, our approach is the first method that achieves the malfunctioning meter detection of specific residential areas with their residents' data in practice. The procedure proposed creatively in this paper mainly consists of four components: data collecting and cleaning, prediction about electricity consumption based on LSTM, sliding window detection, and single user classification based on CNN. To make better classifying of malfunctioned user meters, we combine recurrence plots as image-input and combine them with sequence-input, which is the first work that applies one and two dimensions as two paths CNN's input for sequence data classification. Finally, many classical methods are compared with the method proposed in this paper. After comparison with classical methods, Elastic Net and Gradient Boosting Regression, the result shows that our method has higher accuracy. The average area under the Receiver Operating Characteristic (ROC) curve is 0.80 and the standard deviation is 0.04. The average area under the Precision-Recall Curve (PRC) is 0.84.

READ FULL TEXT

Authors

page 4

09/18/2019

Predicting Electricity Consumption using Deep Recurrent Neural Networks

Electricity consumption has increased exponentially during the past few ...
02/10/2021

An Ensemble Deep Convolutional Neural Network Model for Electricity Theft Detection in Smart Grids

Smart grids extremely rely on Information and Communications Technology ...
10/23/2020

Avoiding Occupancy Detection from Smart Meter using Adversarial Machine Learning

More and more conventional electromechanical meters are being replaced w...
11/08/2019

Data-Driven Multi-step Demand Prediction for Ride-hailing Services Using Convolutional Neural Network

Ride-hailing services are growing rapidly and becoming one of the most d...
07/01/2021

Long-Short Ensemble Network for Bipolar Manic-Euthymic State Recognition Based on Wrist-worn Sensors

Manic episodes of bipolar disorder can lead to uncritical behaviour and ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.