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 learning
Multivariate Data
Analysis
LAMDA
Energetic 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