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Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction

2024-12Artigo científico Acesso aberto
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dc.contributor.authorAlmeida, Fernando
dc.contributor.authorCastelli, Mauro
dc.contributor.authorCôrte-Real, Nadine
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.pblSocieta Italiana di Istochimica / PAGEPress Publications
dc.date.accessioned2024-12-18T16:59:32Z
dc.date.available2024-12-18T16:59:32Z
dc.date.issued2024-12
dc.descriptionAlmeida, F., Castelli, M., & Côrte-Real, N. (2024). Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis. Emerging Science Journal, 8(6), 2120-2143. Article 1. https://doi.org/10.28991/ESJ-2024-08-06-01--- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project UIDB/04152/2020 (doi:10.54499/UIDB/04152/2020)-Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS
dc.description.abstractAccurate cooling consumption forecasts are crucial for optimizing energy management, storage, and overall efficiency in interconnected HVAC systems. Weather conditions, building characteristics, and operational parameters significantly impact prediction accuracy. Since meteorological conditions highly influence cooling demand, leveraging external air data and user metrics offers a promising approach to estimate a building's hourly cooling energy usage. This study addresses the gap in existing research by comprehensively analyzing the performance of various machine learning algorithms, including ensemble learning and deep learning models, to improve prediction accuracy. By leveraging weather conditions, building characteristics, and operational parameters, we aim to predict cooling consumption across multiple systems (Cooling Ceiling, Ventilation, Free Cooling, and Total Cooling). Data from four weather stations, encompassing diverse features relevant to the European Central Bank (ECB) building's cooling consumption in Frankfurt, were employed. Our methodology includes the use of K-Nearest Neighbor, Decision Tree, Support Vector Regression, Linear Regression, Random Forest, Gradient Boosting, XGBoost, Adaboost, Long-Short-Term Memory, and Gated Recurrent Unit. Models. The results consistently demonstrate the superiority of the Random Forest model across different weather stations and feature sets. This model achieved a Mean Squared Error of approximately 0.002-0.003, Mean Absolute Error of around 0.031-0.034, and Root Mean Squared Error of about 0.052-0.069. These findings contribute to improved building cooling load management, promoting insights into optimal energy utilization and sustainable building practices.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent24
dc.format.extent2055697
dc.identifier.doi10.28991/ESJ-2024-08-06-01
dc.identifier.issn2610-9182
dc.identifier.otherPURE: 99415664
dc.identifier.otherPURE UUID: ef24a61f-cc63-4825-b21d-4b82e9c1ef9f
dc.identifier.otherScopus: 85212519775
dc.identifier.otherORCID: /0000-0002-8793-1451/work/173522030
dc.identifier.urihttp://hdl.handle.net/10362/176447
dc.identifier.urlhttps://www.scopus.com/pages/publications/85212519775
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
dc.relationInformation Management Research Center
dc.subjectCooling Loads
dc.subjectMachine Learning
dc.subjectDeep Learning
dc.subjectEnsemble Learning
dc.subjectHVAC Systems
dc.subjectGeneral
dc.subjectSDG 7 - Affordable and Clean Energy
dc.subjectSDG 11 - Sustainable Cities and Communities
dc.titleLeveraging Feature Sets and Machine Learning for Enhanced Energy Load Predictionen
dc.title.subtitleA Comparative Analysisen
dc.typejournal article
degois.publication.firstPage2120
degois.publication.issue6
degois.publication.lastPage2143
degois.publication.titleEmerging Science Journal
degois.publication.volume8
dspace.entity.typePublication
oaire.awardNumberUIDB/04152/2020
oaire.awardTitleInformation Management Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication3274bdb3-4dd3-4bbe-8f74-d34190081f87
relation.isProjectOfPublication.latestForDiscovery3274bdb3-4dd3-4bbe-8f74-d34190081f87

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NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)