Machine learning for green smart homes

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dc.check.date2023-04-22
dc.check.infoAccess to this article is restricted until 12 months after publication by request of the publisher.en
dc.contributor.authorO'Regan, Brian
dc.contributor.authorSilva, Fábio
dc.contributor.authorCarroll, Paula
dc.contributor.authorDubuisson, Xavier
dc.contributor.authorLyons, Pádraig
dc.contributor.funderDepartment of Business, Enterprise and Innovation, Irelanden
dc.date.accessioned2022-10-11T14:44:17Z
dc.date.available2022-10-11T14:44:17Z
dc.date.issued2022-04-22
dc.date.updated2022-10-11T14:21:09Z
dc.description.abstractSmarter approaches to data processing are essential to realise the potential benefits of the exponential growth in energy data in homes from a variety of sources, such as smart metres, sensors and other devices. Machine learning encompasses several techniques to process and visualise data. Each technique is specifically suited to certain data types and problems, whether it be supervised, unsupervised or reinforcement learning. These techniques can be applied to increase the efficient use of energy within a home, enable better and more accurate home owner decision-making and help contribute to greener building stock. This chapter presents the state of the art in this area and looks forward to potential new uses for machine learning in renewable energy data.en
dc.description.sponsorshipDepartment of Business, Enterprise and Innovation, Ireland (Disruptive Technologies Innovation Fund (DTIF))en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationO'Regan, B., Silva, F., Carroll, P., Dubuisson, X. and Lyons, P. (2022) 'Machine learning for green smart homes', in: Lahby, M., Al-Fuqaha, A. and Maleh, Y. (eds.) Computational Intelligence Techniques for Green Smart Cities. Green Energy and Technology. Springer, Cham, pp. 41-66. doi: 10.1007/978-3-030-96429-0_2en
dc.identifier.doi10.1007/978-3-030-96429-0_2en
dc.identifier.eissn1865-3537
dc.identifier.endpage66en
dc.identifier.isbn978-3-030-96429-0
dc.identifier.isbn978-3-030-96428-3
dc.identifier.issn1865-3529
dc.identifier.journaltitleGreen Energy and Technologyen
dc.identifier.startpage41en
dc.identifier.urihttps://hdl.handle.net/10468/13763
dc.language.isoenen
dc.publisherSpringer Nature Switzerland AGen
dc.rights© 2022, the Authors, under exclusive licence to Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of a paper published in Lahby, M., Al-Fuqaha, A. and Maleh, Y. (eds.) Computational Intelligence Techniques for Green Smart Cities. Green Energy and Technology. Springer, Cham, pp. 41-66. doi: 10.1007/978-3-030-96429-0_2. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-96429-0_2en
dc.subjectBig dataen
dc.subjectEnergy managementen
dc.subjectEnergy modellingen
dc.subjectGreen buildingsen
dc.subjectMachine learningen
dc.subjectSmart homeen
dc.titleMachine learning for green smart homesen
dc.typeArticle (peer-reviewed)en
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