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Multi-perspective evaluation of integrated active cooling systems using fuzzy decision making model

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dc.citedby14
dc.contributor.authorAlbahri O.S.en_US
dc.contributor.authorAlamoodi A.H.en_US
dc.contributor.authorDeveci M.en_US
dc.contributor.authorAlbahri A.S.en_US
dc.contributor.authorMahmoud M.A.en_US
dc.contributor.authorSharaf I.M.en_US
dc.contributor.authorCoffman D.M.en_US
dc.contributor.authorid57201013684en_US
dc.contributor.authorid57205435311en_US
dc.contributor.authorid55734383000en_US
dc.contributor.authorid57201009814en_US
dc.contributor.authorid55247787300en_US
dc.contributor.authorid17435789800en_US
dc.contributor.authorid56603101200en_US
dc.date.accessioned2024-10-14T03:17:30Z
dc.date.available2024-10-14T03:17:30Z
dc.date.issued2023
dc.description.abstractAs global median temperatures continue to rise, the demand for active cooling systems (ACs) is increasing. These systems are particularly prevalent in developed countries for maintaining comfort during hot weather. Various ACs technologies are available, and assessing their performance in multi-perspective settings is necessary to determine the best option for intended usage. This requires an evaluation platform for assessment. This paper presents a novel multi-criteria decision-making (MCDM) model based on a new integrated 2-tuple linguistic Pythagorean fuzzy-weighted zero-inconsistency (2 TLP-FWZIC) and modified 2-tuple linguistic Pythagorean fuzzy multi-attributive border approximation area comparison (2TLPF-MABAC). The former is used to determine the importance of assessment criteria, while the latter is employed for selecting the optimal ACs using the obtained weights. The first-level weighting results reveal that performance criteria were predominantly favored for assessment, with �energy performance� acquiring the most significant weight (0.2487) among all performance criteria. In terms of ACs selection results, among the 20 tested and assessed systems, the �geothermal borehole electricity-based ACs� obtained the highest score value (0.1296), while the �window packaged electricity-based ACs� had the lowest score (-0.0515). The robustness of the results was confirmed through sensitivity analysis. � 2023 The Authorsen_US
dc.description.natureFinalen_US
dc.identifier.ArtNo113775
dc.identifier.doi10.1016/j.enpol.2023.113775
dc.identifier.scopus2-s2.0-85168122494
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85168122494&doi=10.1016%2fj.enpol.2023.113775&partnerID=40&md5=da7ba68a57226209272287224545328f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/33951
dc.identifier.volume182
dc.publisherElsevier Ltden_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGreen Open Access
dc.relation.ispartofHybrid Gold Open Access
dc.sourceScopus
dc.sourcetitleEnergy Policy
dc.subject2-Tuple linguistic pythagorean fuzzy sets
dc.subjectClimate change
dc.subjectFWZIC
dc.subjectHousehold energy consumption
dc.subjectIntegrated active cooling systems
dc.subjectMABAC
dc.subjectCooling
dc.subjectCooling systems
dc.subjectDecision making
dc.subjectEnergy utilization
dc.subjectLinguistics
dc.subjectSensitivity analysis
dc.subjectThermoelectric equipment
dc.subject2-tuple linguistic
dc.subject2-tuple linguistic pythagorean fuzzy set
dc.subjectActive cooling
dc.subjectFuzzy Decision making
dc.subjectFWZIC
dc.subjectHousehold energy consumption
dc.subjectIntegrated active cooling system
dc.subjectMABAC
dc.subjectMulti-perspective
dc.subjectPerformance criterion
dc.subjectclimate change
dc.subjectcooling
dc.subjectdecision making
dc.subjectenergy use
dc.subjectfuzzy mathematics
dc.subjecthousehold energy
dc.subjectmulticriteria analysis
dc.subjectClimate change
dc.titleMulti-perspective evaluation of integrated active cooling systems using fuzzy decision making modelen_US
dc.typeArticleen_US
dspace.entity.typePublication
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