Poster Open Access

EMD and Gradient Boosting Regression for NILM (Energy Disaggregation)

Timplalexis, Christos; Krinidis, Stelios; Ioannidis, Dimosthenis; Tzovaras, Dimitrios

Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Timplalexis, Christos</dc:creator>
  <dc:creator>Krinidis, Stelios</dc:creator>
  <dc:creator>Ioannidis, Dimosthenis</dc:creator>
  <dc:creator>Tzovaras, Dimitrios</dc:creator>
  <dc:description>Abstract: In this study a novel appliance load estimation in a non-intrusive way is presented. The proposed algorithm includes signal processing techniques such as filtering and Empirical Mode Decomposition (EMD) which is used to decompose random noise from the power consumption data collected from the smart meter. Lag features that capture the variance of the data across time are utilized. Experimental results which showcase the effectiveness of the suggested method are also presented. </dc:description>
  <dc:title>EMD and Gradient Boosting Regression for NILM (Energy Disaggregation)</dc:title>
All versions This version
Views 2121
Downloads 1818
Data volume 26.0 MB26.0 MB
Unique views 1919
Unique downloads 1616


Cite as