Deep Learning based Smart Meter Data Analytics for Electricity Load Prediction

2019 ◽  
Author(s):  
J Jeyaranjani ◽  
D Devaraj
Keyword(s):  
Deep Learning ◽  
Data Analytics ◽  
Smart Meter ◽  
Load Prediction ◽  
Electricity Load

ELECTRICITY LOAD PREDICTION FOR WATER SUPPLY SYSTEMS

2008 ◽  
Vol 7 (4) ◽  
pp. 453-458
Author(s):  
Mihai Gavrilas ◽  
Gilda Gavrilas ◽  
Ovidiu Ivanov
Keyword(s):  
Water Supply ◽  
Water Supply Systems ◽  
Load Prediction ◽  
Electricity Load ◽  
Supply Systems

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.

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