Low Complexity Non-Intrusive Load Disaggregation of Air Conditioning Unit and Electric Vehicle Charging

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aut.filerelease.date2021-10-24
aut.relation.conference2019 IEEE PES Innovative Smart Grid Technologies Asiaen_NZ
aut.researcherLie, Tek
dc.contributor.authorRehman, AUen_NZ
dc.contributor.authorLie, TTen_NZ
dc.contributor.authorVallès, Ben_NZ
dc.contributor.authorTito, SRen_NZ
dc.date.accessioned2020-03-19T02:22:46Z
dc.date.available2020-03-19T02:22:46Z
dc.date.copyright2019-10-24en_NZ
dc.date.issued2019-10-24en_NZ
dc.description.abstractEnergy monitoring is inevitable towards achieving energy efficiency and conservation. Load disaggregation is one of the techniques towards effective energy monitoring. In the said domain, Non-Intrusive Appliance Load Monitoring (NIALM) is an attractive method where aggregated load data are acquired from a single metering point and segregated appliance level load is estimated using effective software techniques. This paper presents a low complexity event-based NIALM technique based on supervised machine learning. In this paper, the emphasis is on the disaggregation of Air Conditioning (AC) unit and Electric Vehicle (EV) charging loads due to their high significance for the overall power grid stability improvement. A comprehensive digital simulation has been carried out to validate the performance of the proposed approach and intended appliances are aptly classified having an outcome of 97% for same Data ID and 95% for different Data ID in terms of precision, recall, and f-score performance metrics.
dc.identifier.citationIn 2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia) (pp. 2607-2612). IEEE.
dc.identifier.doi10.1109/ISGT-Asia.2019.8881113en_NZ
dc.identifier.isbn978-1-7281-3520-5en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/13213
dc.publisherIEEE
dc.relation.urihttps://ieeexplore.ieee.org/document/8881113
dc.rightsCopyright © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectSmart Meters; Event Detection; Feature Engineering; Supervised Machine Learning; Non-Intrusive Appliance Load Monitoring
dc.titleLow Complexity Non-Intrusive Load Disaggregation of Air Conditioning Unit and Electric Vehicle Chargingen_NZ
dc.typeConference Contribution
pubs.elements-id365537
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Design & Creative Technologies
pubs.organisational-data/AUT/Design & Creative Technologies/Engineering, Computer & Mathematical Sciences
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies/PBRF ECMS
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