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A Transfer Learning Framework for predictive energy-related scenarios in Smart Buildings

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dc.contributor.authorGonzález Vidal, Aurora
dc.contributor.authorNiu, Shuteng
dc.contributor.authorSong, Houbing
dc.contributor.authorSkarmeta Gómez, Antonio
dc.contributor.authorMendoza Bernal, José
dc.contributor.departmentIngeniería de la Información y las Comunicaciones
dc.contributor.otherFacultades de la UMU::Facultad de Informática
dc.date.accessioned2026-02-04T08:20:48Z
dc.date.available2026-02-04T08:20:48Z
dc.date.copyright© 2023, IEEE
dc.date.issued2023-02-01
dc.description.abstractHuman activities and city routines follow patterns. Transfer learning can help achieve scalable solutions towards the realisation of smart cities accounting for similarities between regions, domains, and activities. In this study, we propose a Transfer Learning-based framework for smart buildings to test this hypothesis in energy-related problems. Our framework has two major components: the network creation and the transferable predictive model. In order to create the network that groups buildings sharing characteristics, we evaluated two strategies: a novel clustering algorithm for mixed data, k-prod, and clustering the image-based representation of time series. Then, a combination of Long Short Term Memory and Convolutional Neural Network was trained on the centroids of the clusters for energy consumption prediction. The Coefficient of Variation of the Root Mean Squared Error (CVRMSE) of the predictions in such clusters vary between 3.85 and 58.85 %. The obtained parameters were transferred to the rest of the buildings for predictive purposes, finding accurate results in buildings with little data. Our framework deals with insufficient training data since parameters from scenarios with more sensors can be received. It also carries out state-of-the-art performance on 3 datasets from different sources having in total 533 rooms/buildings and two energy efficiency domains: consumption prediction reducing the CVRMSE in a 21.6 %, and air conditioning usage prediction moving from a 4.18 % to a 0.28% CVRMSE. Our framework extracts more knowledge from available IoT deployments, so that smartness could be spread between environments at a fewer cost given that less individual effort will be needed.
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dc.format.extent12
dc.identifier.citationGonzález-Vidal, A., Mendoza-Bernal, J., Niu, S., Skarmeta, A. F., & Song, H. (2022). A transfer learning framework for predictive energy-related scenarios in smart buildings. IEEE Transactions on Industry Applications, 59(1), 26-37.
dc.identifier.doi10.1109/TIA.2022.3179222
dc.identifier.eissn1939-9367
dc.identifier.issn0093-9994
dc.identifier.urihttp://hdl.handle.net/10201/199049
dc.languageeng
dc.publisherIEEE Transactions on Industry Applications
dc.relationThis work has been sponsored by the European Commission through the H2020 PHOENIX (g.a. 893079) and NGI explorers (g.a. 825183) projects and by ONOFRE Project PID2020-112675RB-C44 funded by MCIN/AEI/10.13039/501100011033. It was also co-financed by the Spanish Ministry of Universities by means of the Margarita Salas linked to the European Union through the NextGenerationEU program and partially supported by the National Science Foundation under Grant No. 2150213 and the Air Force Office of Scientific Research SFFP Program
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9785886?signout=success
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectIoT
dc.subjectk-prod
dc.subjectLSTM
dc.subjectSmart buildings
dc.subjectSmart cities
dc.subjectTransfer learning
dc.subjectCNN
dc.subject.odsObjetivo 11: Ciudades
dc.titleA Transfer Learning Framework for predictive energy-related scenarios in Smart Buildings
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dspace.entity.typePublicationes
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relation.isAuthorOfPublication89099ce3-297a-40dc-9833-322bee8d2fbd
relation.isAuthorOfPublication.latestForDiscoverycf8009bf-6088-449d-9f79-a516af312945
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