Published
Non-Intrusive Electric Load identification using Wavelet Transform
Identificación de cargas eléctricas por medios no invasivos empleando transformada de Wavelet
DOI:
https://doi.org/10.15446/ing.investig.v38n2.70550Keywords:
Non-Intrusive Load Monitoring, Wavelet Transform, Decision Tree (en)Monitoreo de cargas no-invasivo, transformada de ondoleta, árbol de decisión (es)
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.
El presente artículo muestra el desarrollo de un árbol de decisión para la clasificación de cargas en un sistema de monitoreo de cargas no-invasivo (NILM) implementado en un computadora de tarjeta sencillo tipo Raspberry Pi 3. El árbol de decisión emplea el valor de energía total de una señal de potencia de los equipos, el cual es generado empleando una transformada discreta de ondoleta y el teorema de Parseval. Los datos de consumo de potencia de diferentes tipos de equipos fueron obtenidos de una base de datos de acceso público para aplicaciones NILM. El punto de mejor ruptura para el diseño del árbol de decisión se determinó empleando el índice de Gini de promedio ponderado. El árbol fue validado empleando cargas disponibles en la misma base de datas pública.
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Copyright (c) 2018 José Antonio Hoyo-Montaño, Jesús Naim Leon-Ortega, Guillermo Valencia-Palomo, Rafael Armando Galaz-Bustamante, Daniel Fernando Espejel-Blanco, Martín Gustavo Vázquez Palma
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