publication . Other literature type . Conference object . 2019

EMD and Gradient Boosting Regression for NILM (Energy Disaggregation)

Timplalexis, Christos; Krinidis, Stelios; Ioannidis, Dimosthenis; Tzovaras, Dimitrios;
Open Access
  • Published: 29 Oct 2019
  • Publisher: Zenodo
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.
Funded by
Future tamper-proof Demand rEsponse framework through seLf-configured, self-opTimized and collAborative virtual distributed energy nodes
  • Funder: European Commission (EC)
  • Project Code: 773960
  • Funding stream: H2020 | RIA
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Other literature type . 2019
Provider: Datacite
Other literature type . 2019
Provider: Datacite
Conference object . 2019
Provider: ZENODO
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