Event-Detection Algorithms for Low Sampling Nonintrusive Load Monitoring Systems Based on Low Complexity Statistical Features
aut.relation.journal | IEEE Transactions on Instrumentation and Measurement | en_NZ |
aut.researcher | Lie, Tek | |
dc.contributor.author | Rehman, AU | en_NZ |
dc.contributor.author | Lie, T | en_NZ |
dc.contributor.author | Valles, B | en_NZ |
dc.contributor.author | Tito, SR | en_NZ |
dc.date.accessioned | 2019-05-07T03:20:38Z | |
dc.date.available | 2019-05-07T03:20:38Z | |
dc.date.copyright | 2019-04-12 | en_NZ |
dc.date.issued | 2019-04-12 | en_NZ |
dc.description.abstract | One of the key techniques towards energy efficiency and conservation is Non-Intrusive Load Monitoring (NILM) which lies in the domain of energy monitoring. Event detection is a core component of event-based NILM systems. This paper proposes two new low-complexity and computationally fast algorithms that detect the variations of load data and return the time occurrences of the corresponding events. The proposed algorithms are based on the phenomenon of a sliding window that tracks the statistical features of the acquired aggregated load data. The performance of the proposed algorithms is evaluated using real-world data and a comparative analysis has been carried out with one of the recently proposed event detection algorithms. Based on the simulations and sensitivity analysis it is shown that the proposed algorithm can provide the results of up to 93% and 88% in terms of recall and precision respectively. | |
dc.identifier.citation | IEEE Transactions on Instrumentation and Measurement, doi: 10.1109/TIM.2019.2904351 | |
dc.identifier.doi | 10.1109/TIM.2019.2904351 | |
dc.identifier.issn | 0018-9456 | en_NZ |
dc.identifier.uri | https://hdl.handle.net/10292/12494 | |
dc.publisher | Institute of Electrical and Electronics Engineers | en_NZ |
dc.relation.uri | https://ieeexplore.ieee.org/document/8686047 | |
dc.rights | Copyright © 2019 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.accessrights | OpenAccess | en_NZ |
dc.subject | Energy Monitoring; Event Detection; Non-Intrusive Load Monitoring; Smart Grids | |
dc.title | Event-Detection Algorithms for Low Sampling Nonintrusive Load Monitoring Systems Based on Low Complexity Statistical Features | en_NZ |
dc.type | Journal Article | |
pubs.elements-id | 358378 | |
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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