Selective control model of DSM in residential area based on load monitoring system

2011 ◽  
Author(s):  
Chen Lin ◽  
Zhenyu Wang
Keyword(s):  
Monitoring System ◽  
Control Model ◽  
Residential Area ◽  
Selective Control ◽  
Load Monitoring

Semi-supervised Intrusive Appliance Load Monitoring in Smart Energy Monitoring System

2021 ◽  
Vol 17 (3) ◽  
pp. 1-20
Author(s):  
Vanh Khuyen Nguyen ◽  
Wei Emma Zhang ◽  
Adnan Mahmood
Keyword(s):  
Supervised Learning ◽  
Monitoring System ◽  
End Users ◽  
Training Data ◽  
Smart Device ◽  
Energy Monitoring ◽  
Load Monitoring ◽  
Benchmark Datasets ◽  
Smart Home Environment ◽  
Smart Appliance

Intrusive Load Monitoring (ILM) is a method to measure and collect the energy consumption data of individual appliances via smart plugs or smart sockets. A major challenge of ILM is automatic appliance identification, in which the system is able to determine automatically a label of the active appliance connected to the smart device. Existing ILM techniques depend on labels input by end-users and are usually under the supervised learning scheme. However, in reality, end-users labeling is laboriously rendering insufficient training data to fit the supervised learning models. In this work, we propose a semi-supervised learning (SSL) method that leverages rich signals from the unlabeled dataset and jointly learns the classification loss for the labeled dataset and the consistency training loss for unlabeled dataset. The samples fit into consistency learning are generated by a transformation that is built upon weighted versions of DTW Barycenter Averaging algorithm. The work is inspired by two recent advanced works in SSL in computer vision and combines the advantages of the two. We evaluate our method on the dataset collected from our developed Internet-of-Things based energy monitoring system in a smart home environment. We also examine the method’s performances on 10 benchmark datasets. As a result, the proposed method outperforms other methods on our smart appliance datasets and most of the benchmarks datasets, while it shows competitive results on the rest datasets.

scholarly journals Analysis of load monitoring system in hydraulic mobile cranes

2017 ◽  
Vol 263 ◽  
pp. 062045
Author(s):  
G Kalairassan ◽  
M Boopathi ◽  
Rijo Mathew Mohan
Keyword(s):  
Monitoring System ◽  
Load Monitoring

Feature Extraction for Non-intrusive Load Monitoring System

2021 ◽  
Author(s):  
Qiuzhan Zhou ◽  
Jiahui Wei ◽  
Mingyu Sun ◽  
Cong Wang ◽  
Jing Rong ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Monitoring System ◽  
Load Monitoring

The Design of Wireless Load Monitoring System of Auxiliary Shift Based on Labview

2012 ◽  
Vol 182-183 ◽  
pp. 753-757
Author(s):  
Xing Ming Xiao ◽  
Na Ma
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Software Design ◽  
Design Principle ◽  
Monitor System ◽  
Actual Load ◽  
Load Monitoring ◽  
Working Principle ◽  
Formation Design

According to the working principle of load monitored oil pressure, in order to real-time monitor the actual load of auxiliary shift, and make the execution of alarming on the malfunctions in the working state of the equipment concerned, we designed a monitor system of auxiliary shift based on Labview[1]. This system can provide guarantee of the safety lifting. So the formation, design principle, hardware and software design well be introduced in this article.

scholarly journals Load Monitoring System of Electric Appliances Based on Hidden Markov Model

2006 ◽  
Vol 126 (12) ◽  
pp. 1223-1229 ◽  
Author(s):  
Hisahide Nakamura ◽  
Koichi Ito ◽  
Tatsuya Suzuki
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Monitoring System ◽  
Hidden Markov ◽  
Load Monitoring

The JET real-time plasma-wall load monitoring system

2014 ◽  
Vol 89 (3) ◽  
pp. 243-258 ◽  
Author(s):  
D.F. Valcárcel ◽  
D. Alves ◽  
P. Card ◽  
B.B. Carvalho ◽  
S. Devaux ◽  
...  
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Load Monitoring
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