Print Email Facebook Twitter A semi-supervised learning approach to study the energy consumption in smart buildings Title A semi-supervised learning approach to study the energy consumption in smart buildings Author Quintero Gull, C. Aguilar, J. Rodriguez Moreno, M.D. Publication year 2021 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 atasets of energetic consumption are used, with different percent of labeled data, showing very encouraging results according to two metrics in the semi-supervised context. Subject Semi-supervised learningMultivariate DataAnalysisLAMDAEnergetic Consumption To reference this document use: http://resolver.tudelft.nl/uuid:a33870f2-bb09-433e-9e66-a924036f014a TNO identifier 966323 Source IEEE Symposium Series on Computational Intelligence Document type article Files To receive the publication files, please send an e-mail request to TNO Library.