Multi-nodal short-term energy forecasting using smart meter data

dc.contributor.authorHayes, Barry P.
dc.contributor.authorGruber, Jorn K.
dc.contributor.authorProdanovic, Milan
dc.contributor.funderSeventh Framework Programmeen
dc.contributor.funderFP7 People: Marie-Curie Actionsen
dc.date.accessioned2019-07-05T10:42:53Z
dc.date.available2019-07-05T10:42:53Z
dc.date.issued2018-05-21
dc.date.updated2019-07-03T09:12:09Z
dc.description.abstractThis paper deals with the short-term forecasting of electrical energy demands at the local level, incorporating advanced metering infrastructure (AMI), or `smart meter' data. It provides a study of the effects of aggregation on electrical energy demand modelling and multi-nodal demand forecasting. This paper then presents a detailed assessment of the variables which affect electrical energy demand, and how these effects vary at different levels of demand aggregation. Finally, this study outlines an approach for incorporating AMI data in short-term forecasting at the local level, in order to improve forecasting accuracy for applications in distributed energy systems, microgrids and transactive energy. The analysis presented in this study is carried out using large AMI data sets comprised of recorded demand and local weather data from test sites in two European countries.en
dc.description.sponsorshipEuropean Commission (Seventh Framework Programme (Marie Curie researcher mobility action (grant no. FP7-PEOPLE-2013-COFUND))en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationHayes, B. P., Gruber, J. K. and Prodanovic, M. (2018) 'Multi-nodal short-term energy forecasting using smart meter data', IET Generation, Transmission & Distribution, 12(12), pp. 2988-2994. doi: 10.1049/iet-gtd.2017.1599en
dc.identifier.doi10.1049/iet-gtd.2017.1599en
dc.identifier.eissn1751-8695
dc.identifier.endpage2994en
dc.identifier.issn1751-8687
dc.identifier.issued12en
dc.identifier.journaltitleIET Generation, Transmission & Distributionen
dc.identifier.startpage2988en
dc.identifier.urihttps://hdl.handle.net/10468/8118
dc.identifier.volume12en
dc.language.isoenen
dc.publisherInstitution of Engineering and Technology (IET)en
dc.relation.projectinfo:eu-repo/grantAgreement/EC/FP7::SP1::ICT/317761/EU/Energy Demand Aware Open Services for Smart Grid Intelligent Automation/SMARTHGen
dc.relation.urihttps://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2017.1599
dc.rights© The Institution of Engineering and Technology 2018. This paper is a postprint of a paper submitted to and accepted for publication in IET Generation, Transmission and Distribution and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Libraryen
dc.subjectDistributed power generationen
dc.subjectLoad forecastingen
dc.subjectSmart metersen
dc.subjectMultinodal short-term energy forecastingen
dc.subjectSmart meter dataen
dc.subjectAdvanced metering infrastructureen
dc.subjectAMIen
dc.subjectElectrical energy demand modellingen
dc.subjectMultinodal demand forecastingen
dc.subjectDistributed energy systemen
dc.subjectMicrogriden
dc.subjectTransactive energyen
dc.subjectEuropean countryen
dc.subjectPower system measurement and meteringen
dc.subjectPower system planning and layouten
dc.titleMulti-nodal short-term energy forecasting using smart meter dataen
dc.typeArticle (peer-reviewed)en
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