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dc.contributor.authorHock, Den
dc.contributor.authorKappes, Men
dc.contributor.authorGhita, Ben
dc.date.accessioned2021-05-18T10:45:35Z
dc.date.issued2020-06-30en
dc.identifier.urihttp://hdl.handle.net/10026.1/17124
dc.description.abstract

<jats:p>Smart Meters provide detailed energy consumption data and rich contextual information that can be utilized to assist electricity providers and consumers in understanding and managing energy use. The detection of human activity in residential households is a valuable extension for applications, such as home automation, demand side management, or non-intrusive load monitoring, but it usually requires the installation of dedicated sensors. In this paper, we propose and evaluate two new metrics, namely the sliding window entropy and the interval entropy, inspired by Shannon’s entropy in order to obtain information regarding human activity from smart meter readings. We emphasise on the application of the entropy and analyse the effect of input parameters, in order to lay the foundation for future work. We compare our method to other methods, including the Page–Hinkley test and geometric moving average, which have been used for occupancy detection on the same dataset by other authors. Our experimental results, using the power measurements of the publicly available ECO dataset, indicate that the accuracy and area under the curve of our method can keep up with other well-known statistical methods, stressing the practical relevance of our approach.</jats:p>

en
dc.format.extent731 - 731en
dc.languageenen
dc.language.isoenen
dc.publisherMDPI AGen
dc.titleEntropy-Based Metrics for Occupancy Detection Using Energy Demanden
dc.typeJournal Article
plymouth.issue7en
plymouth.volume22en
plymouth.journalEntropyen
dc.identifier.doi10.3390/e22070731en
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2020-06-29en
dc.rights.embargodate2021-05-20en
dc.identifier.eissn1099-4300en
dc.rights.embargoperiodNot knownen
rioxxterms.versionofrecord10.3390/e22070731en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2020-06-30en
rioxxterms.typeJournal Article/Reviewen


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