Application of time series models for heating degree day forecasting

dc.contributor.authorKuru M.
dc.contributor.authorCalis G.
dc.date.accessioned2021-05-03T20:54:12Z
dc.date.available2021-05-03T20:54:12Z
dc.date.issued2020
dc.description.abstractThis study aims at constructing short-term forecast models by analyzing the patterns of the heating degree day (HDD). In this context, two different time series analyses, namely the decomposition and Box–Jenkins methods, were conducted. The monthly HDD data in France between 1974 and 2017 were used for analyses. The multiplicative model and 79 SARIMA models were constructed by the decomposition and Box–Jenkins method, respectively. The performance of the SARIMA models was assessed by the adjusted R2 value, residual sum of squares, the Akaike Information Criteria, the Schwarz Information Criteria, and the analysis of the residuals. Moreover, the mean absolute percentage error, mean absolute deviation, and mean squared deviation values were calculated to evaluate the performance of both methods. The results show that the decomposition method yields more acceptable forecasts than the Box–Jenkins method for supporting short-term forecasting of the HDD. © 2020 Kuru and Calis, published by Sciendo.en_US
dc.identifier.doi10.2478/otmcj-2020-0009
dc.identifier.endpage2146en_US
dc.identifier.issn1847-6228
dc.identifier.issn1847-6228en_US
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85101515791en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage2137en_US
dc.identifier.urihttps://doi.org/10.2478/otmcj-2020-0009
dc.identifier.urihttps://hdl.handle.net/11454/71238
dc.identifier.volume12en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherDe Gruyter Open Ltden_US
dc.relation.ispartofOrganization, Technology and Management in Constructionen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBox–Jenkins methoden_US
dc.subjectHeating degree daysen_US
dc.subjectSARIMA modelsen_US
dc.subjectShort-term forecastingen_US
dc.subjectTime seriesen_US
dc.titleApplication of time series models for heating degree day forecastingen_US
dc.typeArticleen_US

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