A Large-Scale Wireless Network Approach for Intelligent and Automated Meter Reading of Residential Electricity

2010 ◽  
pp. 20-32
Author(s):  
Victor Custodio ◽  
Jose Ignacio Moreno ◽  
Juan Pablo Viñuela
Keyword(s):  
Wireless Network ◽  
Large Scale ◽  
Network Approach ◽  
Residential Electricity ◽  
Automated Meter Reading

scholarly journals Kumar’s, Zipf’s and other laws: how to structure a large-scale wireless network?

2008 ◽  
Vol 63 (5-6) ◽  
pp. 239-251 ◽  
Author(s):  
M. Dohler ◽  
T. Watteyne ◽  
F. Valois ◽  
J.-L. Lu
Keyword(s):  
Wireless Network ◽  
Large Scale

scholarly journals Online Recognition Method for Voltage Sags Based on a Deep Belief Network

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 43 ◽  
Author(s):  
Fei Mei ◽  
Yong Ren ◽  
Qingliang Wu ◽  
Chenyu Zhang ◽  
Yi Pan ◽  
...  
Keyword(s):  
Large Scale ◽  
Deep Belief Network ◽  
Support Vector ◽  
Voltage Sag ◽  
Voltage Sags ◽  
Data Sampling ◽  
Recognition Method ◽  
Belief Network ◽  
Online Recognition ◽  
Residential Electricity

Voltage sag is a serious power quality phenomenon that threatens industrial manufacturing and residential electricity. A large-scale monitoring system has been established and continually improved to detect and record voltage sag events. However, the inefficient process of data sampling cannot provide valuable information early enough for governance of the system. Therefore, a novel online recognition method for voltage sags is proposed. The main contributions of this paper include: 1) The causes and waveform characters of voltage sags were analyzed; 2) according to the characters of different sag waveforms, 10 voltage sag characteristic parameters were proposed and proven to be effective; 3) a deep belief network (DBN) model was built using these parameters to complete automatic recognition of the sag event types. Experiments were conducted using voltage sag data from one month recorded by the 10 kV monitoring points in Suqian, Jiangsu Province, China. The results showed good performance of the proposed method: Recognition accuracy was 96.92%. The test results from the proposed method were compared to the results from support vector machine (SVM) recognition methods. The proposed method was shown to outperform SVM.

scholarly journals Residential electricity conservation in response to auto-generated, multi-featured, personalized eco-feedback designed for large scale applications with utilities

2021 ◽  
Vol 232 ◽  
pp. 110652
Author(s):  
Christoph J. Meinrenken ◽  
Sanjmeet Abrol ◽  
Gaurav B. Gite ◽  
Christopher Hidey ◽  
Kathleen McKeown ◽  
...  
Keyword(s):  
Large Scale ◽  
Residential Electricity ◽  
Electricity Conservation

Neural network approach to a colorimetric value transform based on a large-scale spectral dataset

2016 ◽  
Vol 133 (1) ◽  
pp. 73-80 ◽  
Author(s):  
Qiang Liu ◽  
Xiaoxia Wan ◽  
Jinxing Liang ◽  
Zhen Liu ◽  
Dehong Xie ◽  
...  
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Network Approach ◽  
Neural Network Approach

scholarly journals Developing a Mixed Neural Network Approach to Forecast the Residential Electricity Consumption Based on Sensor Recorded Data

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1443 ◽  
Author(s):  
Simona-Vasilica Oprea ◽  
Alexandru Pîrjan ◽  
George Căruțașu ◽  
Dana-Mihaela Petroșanu ◽  
Adela Bâra ◽  
...  
Keyword(s):  
Neural Network ◽  
Electricity Consumption ◽  
Network Approach ◽  
Neural Network Approach ◽  
Residential Electricity ◽  
Recorded Data ◽  
Residential Electricity Consumption
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