Comparative Analysis of Clustering Techniques for a Hybrid Model Implementation
- Maite García-Ordás 1
- Héctor Alaiz Moretón 1
- José-Luis Casteleiro-Roca 2
- Esteban Jove Pérez 2
- José Alberto Benítez Andrades 1
- Isaías García Rodríguez 1
- Héctor Quintián Pardo 2
- José Luis Calvo Rolle 2
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1
Universidad de León
info
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2
Universidade da Coruña
info
- Álvaro Herrero Cosío (ed. lit.)
- Carlos Cambra Baseca (ed. lit.)
- Daniel Urda Muñoz (ed. lit.)
- Javier Sedano Franco (ed. lit.)
- Héctor Quintián Pardo (ed. lit.)
- Emilio Santiago Corchado Rodríguez (ed. lit.)
Publisher: Springer Suiza
ISBN: 978-3-030-57801-5
Year of publication: 2021
Pages: 355-365
Congress: International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO (15. 2020. Burgos)
Type: Conference paper
Abstract
This research is oriented to compare the performance of two clustering methods in order to know what is the best one for archiving high quality hybrid models. For testing purposes, a real dataset has been obtained of a bio-climate house located in Sotavento Experimental Wind Farm, in Xermade (Lugo) in Galicia (Spain). Between several systems installed in the house, the thermal solar generation system has been the chosen one for studying its behaviour and experimenting with the clustering techniques.Two approaches have been utilized for establishing the quality of each clustering algorithm. The first one is based on typical error measurements implied in a regression procedure such as Multi Layer Perceptron. Whereas, the second one, is oriented to the utilization of three unsupervised learning metrics (Silhouette, Calinski-Harabasz and Davies-Bouldin).