Non-Intrusive Electric Load identification using Wavelet Transform
This paper shows the development of a decision tree for the classification of loads in a non-intrusive load monitoring (NILM) system implemented in a simple board computer (Raspberry Pi 3). The decision tree uses the total energy value of the power signal of an equipment, which is generated using a...
- Autores:
-
Hoyo-Montaño, José Antonio
Leon-Ortega, Jesús Naim
Valencia-Palomo, Guillermo
Galaz-Bustamante, Rafael Armando
Espejel-Blanco, Daniel Fernando
Vázquez Palma, Martín Gustavo
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2018
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/67538
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/67538
http://bdigital.unal.edu.co/68567/
- Palabra clave:
- 62 Ingeniería y operaciones afines / Engineering
Monitoreo de cargas no-invasivo
transformada de ondoleta
árbol de decisión
Non-Intrusive Load Monitoring
Wavelet Transform
Decision Tree
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | This paper shows the development of a decision tree for the classification of loads in a non-intrusive load monitoring (NILM) system implemented in a simple board computer (Raspberry Pi 3). The decision tree uses the total energy value of the power signal of an equipment, which is generated using a discrete wavelet transform and Parseval’s theorem. The power consumption data of different types of equipment were obtained from a public access database for NILM applications. The best split point for the design of the decision tree was determined using the weighted average Gini index. The tree was validated using loads available in the same public access database. |
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