Poster Open Access

EMD and Gradient Boosting Regression for NILM (Energy Disaggregation)

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


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.5281/zenodo.3706433</identifier>
  <creators>
    <creator>
      <creatorName>Timplalexis, Christos</creatorName>
      <givenName>Christos</givenName>
      <familyName>Timplalexis</familyName>
      <affiliation>Information Technologies Institute/ The Centre for Research and Technology Hellas</affiliation>
    </creator>
    <creator>
      <creatorName>Krinidis, Stelios</creatorName>
      <givenName>Stelios</givenName>
      <familyName>Krinidis</familyName>
      <affiliation>Information Technologies Institute/ The Centre for Research and Technology Hellas</affiliation>
    </creator>
    <creator>
      <creatorName>Ioannidis, Dimosthenis</creatorName>
      <givenName>Dimosthenis</givenName>
      <familyName>Ioannidis</familyName>
      <affiliation>Information Technologies Institute/ The Centre for Research and Technology Hellas</affiliation>
    </creator>
    <creator>
      <creatorName>Tzovaras, Dimitrios</creatorName>
      <givenName>Dimitrios</givenName>
      <familyName>Tzovaras</familyName>
      <affiliation>Information Technologies Institute/ The Centre for Research and Technology Hellas</affiliation>
    </creator>
  </creators>
  <titles>
    <title>EMD and Gradient Boosting Regression for NILM (Energy Disaggregation)</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <dates>
    <date dateType="Issued">2019-10-29</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Poster</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3706433</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3706432</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;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.&amp;nbsp;&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/773960/">773960</awardNumber>
      <awardTitle>Future tamper-proof Demand rEsponse framework through seLf-configured, self-opTimized and collAborative virtual distributed energy nodes</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
21
18
views
downloads
All versions This version
Views 2121
Downloads 1818
Data volume 26.0 MB26.0 MB
Unique views 1919
Unique downloads 1616

Share

Cite as