Recent Trends in AI‐Driven Energy Efficiency for Residential Buildings: A Comprehensive Bibliometric Review With a Latent Dirichlet Allocation Topic Modelling

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Atik Ş., Doguc K., Arslan D., Sevindik S.

INTERNATIONAL JOURNAL OF ENERGY RESEARCH, cilt.8515168, ss.1-25, 2026 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 8515168
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1155/er/8515168
  • Dergi Adı: INTERNATIONAL JOURNAL OF ENERGY RESEARCH
  • Derginin Tarandığı İndeksler: Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Earth, Atmospheric, & Aquatic Science Collection (ProQuest), Engineering Source (EBSCO), Scopus, Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest), Aerospace Database, Science Citation Index Expanded (SCI-EXPANDED), Compendex, Environment Index, INSPEC, Directory of Open Access Journals
  • Sayfa Sayıları: ss.1-25
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Erciyes Üniversitesi Adresli: Evet

Özet

Artificial Intelligence(AI)hasrapidlyreshapedresidentialbuildingenergyresearch;however,the field lacksadata-driven

synthesis ofitsglobalstructureandevolvingthemes.Thisstudyaddressesthisgapbycombiningbibliometricanalysiswith

latent Dirichletallocation(LDA)topicmodellingtoanalyse1368articles(2020–2025) fromWebofScienceCoreCollection

(WoSCC) andScopus.Resultsshowadramaticincreaseinpublicationswithinaglobalbutunevenlandscape:Chinaandthe

United States(US)leadinvolume,whileVietnamandDenmarkproducethemostinfluential outputsperpaper.TheUnited

Kingdom (UK)maintainsabalancedprofile, andseveralEUmemberstates,includingItaly,SpainandGermany,rankamongthe

leading contributors.Collaborationnetworksareprimarilyacademic,withlimitedindustryinvolvement.LDAidentifies four

main themes:energyperformancemodelling,HVACoptimisation,loadforecastingandmanagementandsmart-homeenergy

management. Withinthese,researchcentresonoperationalapplicationsandshowsamethodologicalprogressionfromconven-

tional machinelearning(ML)towardhybriddeeplearning(DL),includingemergingtransformer-basedandgraph-basedmodels.

The analysisscaleisexpandingfromsingle-buildingstodistrict-levelandinterconnectedsystems.Thecomprehensivereview

identifies apersistentgapinoccupant-centricmodellingandhighlightstheneedforreal-timedataintegrationandimproved

transparency todeliverpractical,human-responsive,andscalableenergyefficiency solutions.