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dc.contributor.authorRehman, AUen_NZ
dc.contributor.authorLie, Ten_NZ
dc.contributor.authorValles, Ben_NZ
dc.contributor.authorTito, SRen_NZ
dc.date.accessioned2019-05-07T03:20:38Z
dc.date.available2019-05-07T03:20:38Z
dc.date.copyright2019-04-12en_NZ
dc.identifier.citationIEEE Transactions on Instrumentation and Measurement, doi: 10.1109/TIM.2019.2904351
dc.identifier.issn0018-9456en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/12494
dc.description.abstractOne 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.publisherInstitute of Electrical and Electronics Engineersen_NZ
dc.relation.urihttps://ieeexplore.ieee.org/document/8686047
dc.rightsCopyright © 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.subjectEnergy Monitoring; Event Detection; Non-Intrusive Load Monitoring; Smart Grids
dc.titleEvent-Detection Algorithms for Low Sampling Nonintrusive Load Monitoring Systems Based on Low Complexity Statistical Featuresen_NZ
dc.typeJournal Article
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.1109/TIM.2019.2904351
pubs.elements-id358378
aut.relation.journalIEEE Transactions on Instrumentation and Measurementen_NZ


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