| Title | Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study |
|---|---|
| Authors | 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 |
| External publication | No |
| Means | Electronics (Switzerland) |
| Scope | Article |
| Nature | Científica |
| JCR Quartile | 2 |
| SJR Quartile | 2 |
| JCR Impact | 2.9 |
| SJR Impact | 0.628 |
| Web | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122880481&doi=10.3390%2felectronics11020267&partnerID=40&md5=c88498d53b868476b9b7cfaad5d4eb3e |
| Publication date | 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. |
| Keywords | Clustering; Distribution network; Energy; Feeder |
| Universidad Loyola members |