Journal
ENERGIES
Volume 13, Issue 17, Pages -Publisher
MDPI
DOI: 10.3390/en13174396
Keywords
event detection; feature extraction; load classification; NILM; non-intrusive load monitoring; NILM architecture
Categories
Funding
- Agencia Nacional de Energia Eletrica (ANEEL)
- Companhia Paranaense de Energia Eletrica (COPEL) under the research and development program [PD2866-0464/2017]
Ask authors/readers for more resources
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%.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available