scholarly journals Sequential Clustering-Based Event Detection for Non-Intrusive Load Monitoring

2016 ◽  
Author(s):  
Karim Said Barsim ◽  
Bin Yang
Keyword(s):  
Event Detection ◽  
Load Monitoring ◽  
Sequential Clustering

High accuracy event detection for Non-Intrusive Load Monitoring

2017 ◽  
Author(s):  
Mohamed Nait Meziane ◽  
Philippe Ravier ◽  
Guy Lamarque ◽  
Jean-Charles Le Bunetel ◽  
Yves Raingeaud
Keyword(s):  
Event Detection ◽  
High Accuracy ◽  
Load Monitoring

scholarly journals A Multi-Agent NILM Architecture for Event Detection and Load Classification

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4396
Author(s):  
André Eugenio Lazzaretti ◽  
Douglas Paulo Bertrand Renaux ◽  
Carlos Raimundo Erig Lima ◽  
Bruna Machado Mulinari ◽  
Hellen Cristina Ancelmo ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Event Detection ◽  
Classification Accuracy ◽  
Agent Architecture ◽  
Performance Improvements ◽  
Load Monitoring ◽  
Multi Agent ◽  
Improved Performance ◽  
New Algorithms

A multi-agent architecture for a Non-Intrusive Load Monitoring (NILM) solution is presented and evaluated. The underlying rationale for such an architecture is that each agent (load event detection, feature extraction, and classification) outperforms others of the same type in particular scenarios; hence, by combining the expertise of these agents, the system presents an improved performance. Known NILM algorithms, as well as new algorithms, proposed by the authors, were individually evaluated and compared. The proposed architecture considers a NILM system composed of Load Monitoring Modules (LMM) that report to a Center of Operations, required in larger facilities. For the purposed of evaluating and comparing performance, five load event detect agents, five feature extraction agents, and five classification agents were studied so that the best combinations of agents could be implemented in LMMs. To evaluate the proposed system, the COOLL and the LIT-Dataset were used. Performance improvements were detected in all scenarios, with power-ON and power-OFF detection improving up to 13%, while classification accuracy improved up to 9.4%.

Sign in / Sign up

Export Citation Format

Share Document