Título Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study
Autores Morales F. , García-Torres M. , Velázquez G. , Daumas-Ladouce F. , Gardel-Sotomayor P.E. , Gómez-Vela F. , Divina F. , Vázquez Noguera J.L. , Sauer Ayala C. , Pinto-Roa D.P. , Mello-Román J.C. , BECERRA ALONSO, DAVID
Publicación externa No
Alcance Article
Naturaleza Científica
Cuartil JCR 2
Cuartil SJR 2
Impacto JCR 2.9
Impacto SJR 0.628
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122880481&doi=10.3390%2felectronics11020267&partnerID=40&md5=c88498d53b868476b9b7cfaad5d4eb3e
Fecha de publicacion 14/01/2022
ISI 000757670800001
Scopus Id 2-s2.0-85122880481
DOI 10.3390/electronics11020267
Abstract Correctly defining and grouping electrical feeders is of great importance for electrical system operators. In this paper, we compare two different clustering techniques, K-means and hierarchical agglomerative clustering, applied to real data from the east region of Paraguay. The raw data were pre-processed, resulting in four data sets, namely, (i) a weekly feeder demand, (ii) a monthly feeder demand, (iii) a statistical feature set extracted from the original data and (iv) a seasonal and daily consumption feature set obtained considering the characteristics of the Paraguayan load curve. Considering the four data sets, two clustering algorithms, two distance metrics and five linkage criteria a total of 36 models with the Silhouette, Davies–Bouldin and Calinski–Harabasz index scores was assessed. The K-means algorithms with the seasonal feature data sets showed the best performance considering the Silhouette, Calinski–Harabasz and Davies–Bouldin validation index scores with a configuration of six clusters. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Palabras clave Clustering; Distribution network; Energy; Feeder
Miembros de la Universidad Loyola

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