Deep Learning-Based Consumer Profiling from Smart Meter Data

2021 ◽  
Author(s):  
Pamuda Balasuriya ◽  
Ranilka Perera ◽  
Sachini Rajapaksha ◽  
Janaka Wijayakulasooriya ◽  
Janaka Ekanayake ◽  
...  
Keyword(s):  
Deep Learning ◽  
Smart Meter ◽  
Consumer Profiling

scholarly journals Evaluation Model of Operation State Based on Deep Learning for Smart Meter

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4674
Author(s):  
Qingsheng Zhao ◽  
Juwen Mu ◽  
Xiaoqing Han ◽  
Dingkang Liang ◽  
Xuping Wang
Keyword(s):  
Deep Learning ◽  
Power Consumption ◽  
Detection Efficiency ◽  
Evaluation Model ◽  
Smart Meter ◽  
Smart Meters ◽  
Abnormal Detection ◽  
Prediction Residual ◽  
Threshold Setting ◽  
Training Efficiency

The operation state detection of numerous smart meters is a significant problem caused by manual on-site testing. This paper addresses the problem of improving the malfunction detection efficiency of smart meters using deep learning and proposes a novel evaluation model of operation state for smart meter. This evaluation model adopts recurrent neural networks (RNN) to predict power consumption. According to the prediction residual between predicted power consumption and the observed power consumption, the malfunctioning smart meter is detected. The training efficiency for the prediction model is improved by using transfer learning (TL). This evaluation uses an accumulator algorithm and threshold setting with flexibility for abnormal detection. In the simulation experiment, the detection principle is demonstrated to improve efficient replacement and extend the average using time of smart meters. The effectiveness of the evaluation model was verified on the actual station dataset. It has accurately detected the operation state of smart meters.

scholarly journals Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network

2021 ◽  
Vol 282 ◽  
pp. 116177
Author(s):  
Mohammad Navid Fekri ◽  
Harsh Patel ◽  
Katarina Grolinger ◽  
Vinay Sharma
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Load Forecasting ◽  
Smart Meter

scholarly journals Prediction of voltage distribution using deep learning and identified key smart meter locations

Energy and AI ◽  
2021 ◽  
pp. 100103
Author(s):  
Maizura Mokhtar ◽  
Valentin Robu ◽  
David Flynn ◽  
Ciaran Higgins ◽  
Jim Whyte ◽  
...  
Keyword(s):  
Deep Learning ◽  
Smart Meter ◽  
Voltage Distribution

scholarly journals Automatic Detection of Display Defects for Smart Meters based on Deep Learning

2021 ◽  
Vol 28 (4) ◽  
pp. 241-254
Author(s):  
Ye Chen ◽  
Zhihu Hong ◽  
Yaohua Liao ◽  
Mengmeng Zhu ◽  
Tong Han ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Detection ◽  
Defect Detection ◽  
Automatic Detection ◽  
Detection Task ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Smart Meter ◽  
Smart Meters ◽  
The Common

The smart meter is an essential part of an intelligent grid system. Defects in the LCD screen the smart meters affect their use. Therefore, detection of LCD screen defects of smart meters is of great significance for management and use of smart electricity meters. At present, detection methods are mainly realized by manual detection and automatic detection based on machine vision. However, performance of these two methods is not satisfactory. The fault detection task of a smart meter LCD screen can be divided into two parts: smart meter LCD localization and LCD fault detection. Therefore, this paper proposes a twostage system based on deep learning, which combines YOLOv5 with ResNet34. YOLOv5 is used for smart meter LCD localization and the classification network based on ResNet34 for LCD fault detection. We have constructed an LCD screen localization dataset and an LCD screen defect detection dataset to train and test our model. As a result, our model achieves a defect detection accuracy of 98.9% on the dataset proposed in this paper and can accurately detect the common defects of an LCD screen.

Deep Learning-Based Socio-Demographic Information Identification From Smart Meter Data

2019 ◽  
Vol 10 (3) ◽  
pp. 2593-2602 ◽  
Author(s):  
Yi Wang ◽  
Qixin Chen ◽  
Dahua Gan ◽  
Jingwei Yang ◽  
Daniel S. Kirschen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Smart Meter ◽  
Demographic Information
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