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 ◽  
...  
Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4674
Author(s):  
Qingsheng Zhao ◽  
Juwen Mu ◽  
Xiaoqing Han ◽  
Dingkang Liang ◽  
Xuping Wang

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.


Author(s):  
Izaskun Mendia ◽  
Sergio Gil-López ◽  
Javier Del Ser ◽  
Ana González Bordagaray ◽  
Jesús García Prado ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document