Functional time series approach for forecasting very short-term electricity demand

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dc.contributor.authorShang, Hanlin
dc.date.accessioned2020-01-07T04:59:44Z
dc.date.issued2013
dc.date.updated2019-08-18T08:16:02Z
dc.description.abstractThis empirical paper presents a number of functional modelling and forecasting methods for predicting very short-term (such as minute-by-minute) electricity demand. The proposed functional methods slice a seasonal univariate time series (TS) into a TS of curves; reduce the dimensionality of curves by applying functional principal component analysis before using a univariate TS forecasting method and regression techniques. As data points in the daily electricity demand are sequentially observed, a forecast updating method can greatly improve the accuracy of point forecasts. Moreover, we present a non-parametric bootstrap approach to construct and update prediction intervals, and compare the point and interval forecast accuracy with some naive benchmark methods. The proposed methods are illustrated by the half-hourly electricity demand from Monday to Sunday in South Australia.en_AU
dc.description.sponsorshipThis work was partially supported by the postgraduate publication award of Monash Universityen_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn0266-4763en_AU
dc.identifier.urihttp://hdl.handle.net/1885/196565
dc.language.isoen_AUen_AU
dc.publisherTaylor & Francisen_AU
dc.rights© 2013 Taylor & Francisen_AU
dc.sourceJournal of Applied Statisticsen_AU
dc.subjectfunctional principal component analysisen_AU
dc.subjectmultivariate time seriesen_AU
dc.subjectordinary least-squares regressionen_AU
dc.subjectpenalised least-squares regressionen_AU
dc.subjectroughness penaltyen_AU
dc.subjectseasonal time seriesen_AU
dc.titleFunctional time series approach for forecasting very short-term electricity demanden_AU
dc.typeJournal articleen_AU
local.bibliographicCitation.issue1en_AU
local.bibliographicCitation.lastpage168en_AU
local.bibliographicCitation.startpage152en_AU
local.contributor.affiliationShang, Hanlin, College of Business and Economics, ANUen_AU
local.contributor.authoremailu5506744@anu.edu.auen_AU
local.contributor.authoruidShang, Hanlin, u5506744en_AU
local.description.embargo2037-12-31
local.description.notesImported from ARIESen_AU
local.identifier.absfor010401 - Applied Statisticsen_AU
local.identifier.absfor160305 - Population Trends and Policiesen_AU
local.identifier.ariespublicationa383154xPUB1061en_AU
local.identifier.citationvolume40en_AU
local.identifier.doi10.1080/02664763.2012.740619en_AU
local.identifier.scopusID2-s2.0-84870863987
local.identifier.thomsonID000313029300012
local.identifier.uidSubmittedBya383154en_AU
local.publisher.urlhttps://www.routledge.com/en_AU
local.type.statusPublished Versionen_AU

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