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Deep learning classification approaches and applications for energy performance certificates (EPCs)

2025-12-01Artigo científico Acesso aberto
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dc.contributor.authorAnastasiadou, Maria
dc.contributor.authorSantos, Vítor
dc.contributor.authorDias, Miguel Sales
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.pblElsevier Science B.V., Amsterdam.
dc.date.accessioned2025-11-06T21:46:12Z
dc.date.available2025-11-06T21:46:12Z
dc.date.issued2025-12-01
dc.descriptionAnastasiadou, M., Santos, V., & Dias, M. S. (2025). Deep learning classification approaches and applications for energy performance certificates (EPCs). Energy, 339, Article 139148. https://doi.org/10.1016/j.energy.2025.139148
dc.description.abstractBuilding energy performance classification is a cornerstone of sustainable development initiatives. This study presents an innovative approach leveraging Artificial Neural Networks to classify Energy Performance Certificates. Our Artificial Neural Networks model, integrating the Synthetic Minority Oversampling Technique for class balancing and Principal Component Analysis for dimensionality reduction, achieved a test accuracy of 93.44 %, supported by a macro and weighted F1-score of 0.93, outperforming many existing models and creating a unique sequence and combination of methods to conclude in that result. A detailed analysis of class-level performance underscores its robustness for high-rated energy classes while revealing challenges in differentiating lower-rated classes. This work bridges the gap between high-performance AI models and their interpretability, setting a benchmark for future energy performance certificate classification studies.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent12
dc.format.extent6307517
dc.identifier.doi10.1016/j.energy.2025.139148
dc.identifier.issn0360-5442
dc.identifier.otherPURE: 134811644
dc.identifier.otherPURE UUID: 75354ca6-3c16-4df1-999e-577b0093b8fd
dc.identifier.otherScopus: 105021861704
dc.identifier.otherWOS: 001719079900008
dc.identifier.otherORCID: /0000-0002-4223-7079/work/195955961
dc.identifier.otherORCID: /0000-0003-2770-7025/work/215175654
dc.identifier.urihttp://hdl.handle.net/10362/190240
dc.identifier.urlhttps://www.scopus.com/pages/publications/105021861704
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001719079900008
dc.language.isoeng
dc.peerreviewedyes
dc.relationhttps://doi.org/10.54499/PRT/BD/152840/2021
dc.relationhttps://doi.org/10.54499/UID/04152/2025
dc.relationhttps://doi.org/10.54499/UID/PRR/04152/2025
dc.subjectEnergy Performance Certificates
dc.subjectArtificial Intelligence
dc.subjectDeep learning
dc.subjectArtificial Neural Networks
dc.subjectSynthetic Minority Oversampling Technique
dc.subjectPrincipal Component Analysis
dc.subject7th Sustainable Development Goal
dc.subjectCivil and Structural Engineering
dc.subjectBuilding and Construction
dc.subjectModelling and Simulation
dc.subjectRenewable Energy, Sustainability and the Environment
dc.subjectFuel Technology
dc.subjectEnergy Engineering and Power Technology
dc.subjectPollution
dc.subjectMechanical Engineering
dc.subjectGeneral Energy
dc.subjectIndustrial and Manufacturing Engineering
dc.subjectManagement, Monitoring, Policy and Law
dc.subjectElectrical and Electronic Engineering
dc.subjectSDG 7 - Affordable and Clean Energy
dc.titleDeep learning classification approaches and applications for energy performance certificates (EPCs)en
dc.typejournal article
degois.publication.titleEnergy
degois.publication.volume339
dspace.entity.typePublication
rcaap.rightsopenAccess

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