Poster Open Access

EMD and Gradient Boosting Regression for NILM (Energy Disaggregation)

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

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{
"publisher": "Zenodo",
"DOI": "10.5281/zenodo.3706433",
"author": [
{
"family": "Timplalexis, Christos"
},
{
"family": "Krinidis, Stelios"
},
{
"family": "Ioannidis, Dimosthenis"
},
{
"family": "Tzovaras, Dimitrios"
}
],
"issued": {
"date-parts": [
[
2019,
10,
29
]
]
},
"abstract": "<p>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.&nbsp;</p>",
"title": "EMD and Gradient Boosting Regression for NILM (Energy Disaggregation)",
"type": "graphic",
"id": "3706433"
}
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