A semi-supervised learning approach to study the energy consumption in smart buildings
Identifiers
Permanent link (URI): http://hdl.handle.net/10017/53410DOI: 10.1109/SSCI50451.2021.9659911
ISBN: 978-1-7281-9048-8
Publisher
IEEE
Date
2021-12-01Funders
European Commission
Agencia Estatal de Investigación
Junta de Comunidades de Castilla-La Mancha
Bibliographic citation
Quintero Gull, C., Aguilar Castro, J.L. & Rodriguez Moreno, M.D. 2021, "A semi-supervised learning approach to study the energy consumption in smart buildings", in 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 05-07 Dec. 2021.
Keywords
Semi-supervised learning
Multivariate Data Analysis
LAMDA
Energetic Consumption
Description / Notes
IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021), 05/12/2021-07/12/2021.
Project
info:eu-repo/grantAgreement/EC/H2020/754382/EU/GOT Energy Talent/GET
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109891RB-I00/ES/MEJORA DE LA GESTION DE RECURSOS HOSPITALARIOS MEDIANTE LA PREDICCION DE LA DEMANDA CON APRENDIZAJE AUTOMATICO Y PLANIFICACION/
info:eu-repo/grantAgreement/JCCM//SBPLY%2F19%2F180501%2F000024/ES/MEJORA DE LA GESTIÓN DE RECURSOS HOSPITALARIOS MEDIANTE LA PREDICCIÓN DE LA DEMANDA CON APRENDIZAJE AUTOMÁTICO Y PLANIFICACIÓN
Document type
info:eu-repo/semantics/conferenceObject
Version
info:eu-repo/semantics/acceptedVersion
Publisher's version
https://doi.org/10.1109/SSCI50451.2021.9659911Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
© 2021 IEEE
Access rights
info:eu-repo/semantics/openAccess
Abstract
In this work, we use the semi-supervised LAMDA-HSCC algorithm for characterizing the energy consumption in smart buildings, which can work with labeled and unlabeled data. Particularly, it uses the LAMDA-RD approach for the clustering problem and the LAMDA-HAD approach for the classification problem. Additionally, this algorithm uses three submodels for merging, partition groups (classes/cluster) and migrating individuals from a group to another. For the performance evaluation, several datasets of energetic consumption are used, with different percent of labeled data, showing very encouraging results according to two metrics in the semi-supervised context.
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