University of Hertfordshire

By the same authors

Improving energy disaggregation performance using appliance-driven sampling rates

Research output: Chapter in Book/Report/Conference proceedingConference contribution

View graph of relations
Original languageEnglish
Title of host publicationEUSIPCO 2019 - 27th European Signal Processing Conference
PublisherEuropean Signal Processing Conference, EUSIPCO
ISBN (Electronic)9789082797039
DOIs
Publication statusPublished - Sep 2019
Event27th European Signal Processing Conference, EUSIPCO 2019 - A Coruna, Spain
Duration: 2 Sep 20196 Sep 2019

Publication series

NameEuropean Signal Processing Conference
Volume2019-September
ISSN (Print)2219-5491

Conference

Conference27th European Signal Processing Conference, EUSIPCO 2019
CountrySpain
CityA Coruna
Period2/09/196/09/19

Abstract

This paper proposes a new appliance-driven selection of sampling frequencies for improving the energy disaggregation performance in non-intrusive load monitoring. Specifically, the methodology uses a machine learning model with parallel device detectors and optimized device dependent sampling rates in order to improve device identification. The performance of the proposed methodology was evaluated on a state-of-the-art baseline system and a set of publicly available databases increasing performance up to 6.7% in terms of estimation accuracy when compared to the baseline energy disaggregation setup without device dependent sampling rates.

ID: 19522523