Rodyti trumpą aprašą

dc.contributor.authorD'Amico, Antonino
dc.contributor.authorCiulla, Giuseppina
dc.contributor.authorTupėnaitė, Laura
dc.contributor.authorKaklauskas, Artūras
dc.date.accessioned2023-09-18T20:29:36Z
dc.date.available2023-09-18T20:29:36Z
dc.date.issued2020
dc.identifier.issn0378-7788
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/150380
dc.description.abstractNowadays worldwide directives have focused the attention on improving energy efficiency in the building sector. The research of models able to predict the energy consumption from the first design and energy planning phase is conducted to improve building sustainability. Use of traditional forecasting tools for building thermal energy demand tends to encounter difficulties relevant to the amount of data required, implementation of the models, computational costs and inability to generalize the output. Therefore, many studies focused on the research and development of alternative resolution methods, but the choice of the most convenient is not clear and simple. Single comparison of statistical quality indexes does not allow an adequate identification of the most efficient method, as the necessary efforts for implementation of the methods from the initial data collection to the use phase are not considered. In this work, the authors propose to apply, for the first time, the multicriteria assessment to determine the most efficient alternative method, used for forecasting of building thermal energy demand. Three alternative “black-box” methods, previously investigated by the authors, were compared by the multiple criteria Complex Proportional Assessment Method. Such a procedure revealed ranking of the methods in four phases, namely Pre-processing, Implementation, Post-processing and Use, as well as overall assessment and selection of the most efficient method in terms of evaluated criteria. This first application could represent an incentive for future multi-criteria analyses involving a growing number of alternative forecasting methods.eng
dc.formatPDF
dc.format.extentp. 1-16
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyScience Citation Index Expanded (Web of Science)
dc.relation.isreferencedbyScopus
dc.relation.isreferencedbyCambridge Scientific Abstracts - Conference Papers Index
dc.source.urihttps://doi.org/10.1016/j.enbuild.2020.110220
dc.titleMultiple criteria assessment of methods for forecasting building thermal energy demand
dc.typeStraipsnis Web of Science DB / Article in Web of Science DB
dcterms.references56
dc.type.pubtypeS1 - Straipsnis Web of Science DB / Web of Science DB article
dc.contributor.institutionUniversità degli Studi di Palermo
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.facultyStatybos fakultetas / Faculty of Civil Engineering
dc.subject.researchfieldT 002 - Statybos inžinerija / Construction and engineering
dc.subject.vgtuprioritizedfieldsSD0404 - Statinių skaitmeninis modeliavimas ir tvarus gyvavimo ciklas / BIM and Sustainable lifecycle of the structures
dc.subject.ltspecializationsL102 - Energetika ir tvari aplinka / Energy and a sustainable environment
dc.subject.enbuilding thermal energy demand
dc.subject.enforecasting method
dc.subject.enmultiple linear regression
dc.subject.endimensionless analysis
dc.subject.enartificial neural network
dc.subject.enmultiple criteria assessment
dcterms.sourcetitleEnergy and buildings
dc.description.volumevol. 224
dc.publisher.nameElsevier
dc.publisher.cityLausanne
dc.identifier.doi000570251000007
dc.identifier.doi10.1016/j.enbuild.2020.110220
dc.identifier.elaba64451815


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