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Smart Energy monitoring for sustainable campuses: a hybrid anomaly detection approach based on prophet and Isolation forest

2026-03-06Artigo científico Acesso aberto
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datacite.subject.sdg07:Energias Renováveis e Acessíveis
datacite.subject.sdg11:Cidades e Comunidades Sustentáveis
datacite.subject.sdg13:Ação Climática
dc.contributor.authorSousa, Ângelo
dc.contributor.authorCardoso, Pedro
dc.contributor.authorMonteiro, Jânio
dc.date.accessioned2026-07-14T13:27:12Z
dc.date.available2026-07-14T13:27:12Z
dc.date.issued2026-03-06
dc.description.abstractThe transition towards sustainable educational campuses requires robust energy management strategies that integrate operational oversight with advanced analytics. This paper presents a campus-scale electricity monitoring system at the University of Algarve, designed to support the institution’s sustainability goals through continuous monitoring, data reliability, portability, and scalability to handle concurrent high-frequency campus-wide telemetry. The system consolidates heterogeneous meters into a unified platform, enabling precise tracking of energy consumption and photovoltaic generation. Beyond operational efficiency, the platform incorporates a data-driven analytical layer featuring short-term forecasting using Prophet, chosen for its computational scalability, and a hybrid anomaly detection scheme combining forecast residuals with Isolation Forest. These capabilities facilitate the early identification of waste and abnormal consumption patterns, directly contributing to energy conservation and carbon footprint reduction. Validated across multiple buildings, the system demonstrates both portability to different energy profiles and high data continuity, reducing the cognitive load on facility managers. By providing a reproducible blueprint for intelligent energy monitoring, this work supports institutions in their pursuit of energy efficiency and sustainable development, aligning operational practices with broader environmental objectives.eng
dc.description.sponsorshipUID/04516/2025; (0091_AGERAR_PLUS_6_E) & (0081_CEL_RURAL_6_E_V3) projects.
dc.identifier.doi10.3390/su18052589
dc.identifier.issn2071-1050
dc.identifier.urihttp://hdl.handle.net/10400.1/29270
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relation.ispartofSustainability
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectEnergy monitoring
dc.subjectCampus energy management
dc.subjectTime-series forecasting
dc.subjectAnomaly detection
dc.subjectBuilding management systems
dc.subjectSmart buildings
dc.subjectEnergy efficiency
dc.titleSmart Energy monitoring for sustainable campuses: a hybrid anomaly detection approach based on prophet and Isolation foresteng
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberUID/CEC/04516/2013
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FCEC%2F04516%2F2013/PT
oaire.citation.endPage18
oaire.citation.issue5
oaire.citation.startPage1
oaire.citation.titleSustainability
oaire.citation.volume18
oaire.fundingStream5876
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameSousa
person.familyNameCardoso
person.familyNameMonteiro
person.givenNameÂngelo
person.givenNamePedro
person.givenNameJânio
person.identifierR-001-H74
person.identifier.ciencia-id5F10-1C37-FE45
person.identifier.ciencia-idD019-1CF7-B156
person.identifier.orcid0009-0004-5246-9027
person.identifier.orcid0000-0003-4803-7964
person.identifier.orcid0000-0002-4203-1679
person.identifier.ridG-6405-2013
person.identifier.ridO-3416-2015
person.identifier.scopus-author-id35602693500
person.identifier.scopus-author-id35606413800
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublicationa722ffdf-47b5-4520-bdd3-81d85a974afd
relation.isAuthorOfPublication62bebc54-51ee-4e35-bcf5-6dd69efd09e0
relation.isAuthorOfPublication7701f2af-b9b8-42aa-bb1e-a13e5a4897be
relation.isAuthorOfPublication.latestForDiscovery62bebc54-51ee-4e35-bcf5-6dd69efd09e0
relation.isProjectOfPublication37bdfcd7-b84f-4d11-8fa5-25a959cd5438
relation.isProjectOfPublication.latestForDiscovery37bdfcd7-b84f-4d11-8fa5-25a959cd5438

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