scholarly journals On the Selection of Input Variables for a Wind Turbine Load Monitoring System

2014 ◽  
Vol 15 ◽  
pp. 726-736 ◽  
Author(s):  
Luis Vera-Tudela ◽  
Martin Kühn
Keyword(s):  
Wind Turbine ◽  
Monitoring System ◽  
Load Monitoring ◽  
Input Variables ◽  
Selection Of

scholarly journals Feature selection of non-intrusive load monitoring system using RFE and RF

2019 ◽  
Vol 1176 ◽  
pp. 042053 ◽  
Author(s):  
Zhicheng Zhu ◽  
Zhiqiang Wei ◽  
Bo Yin ◽  
Tao Liu ◽  
Xianqing Huang
Keyword(s):  
Feature Selection ◽  
Monitoring System ◽  
Load Monitoring ◽  
Selection Of

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

Selection of Input Variables for the Prediction of Wind Speed in Wind Farms Based on Genetic Algorithm

2011 ◽  
Vol 35 (6) ◽  
pp. 649-660 ◽  
Author(s):  
R. A. Gupta ◽  
Rajesh Kumar ◽  
Ajay Kumar Bansal
Keyword(s):  
Genetic Algorithm ◽  
Wind Speed ◽  
Wind Farms ◽  
Input Variables ◽  
Selection Of

Wind Turbine Failure Prediction Model using SCADA-based Condition Monitoring System

2021 ◽  
Author(s):  
Bara Alzawaideh ◽  
Payam Teimourzadeh Baboli ◽  
Davood Babazadeh ◽  
Susanne Horodyvskyy ◽  
Isabel Koprek ◽  
...  
Keyword(s):  
Prediction Model ◽  
Wind Turbine ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Failure Prediction ◽  
Condition Monitoring System
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