000153224 001__ 153224
000153224 005__ 20251017144649.0
000153224 0247_ $$2doi$$a10.3390/en18071832
000153224 0248_ $$2sideral$$a143717
000153224 037__ $$aART-2025-143717
000153224 041__ $$aeng
000153224 100__ $$aTorres-Bermeo, Pedro
000153224 245__ $$aSizing and Characterization of Load Curves of Distribution Transformers Using Clustering and Predictive Machine Learning Models
000153224 260__ $$c2025
000153224 5060_ $$aAccess copy available to the general public$$fUnrestricted
000153224 5203_ $$aThe efficient sizing and characterization of the load curves of distribution transformers are crucial challenges for electric utilities, especially given the increasing variability of demand, driven by emerging loads such as electric vehicles. This study applies clustering techniques and predictive models to analyze and predict the behavior of transformer demand, optimize utilization factors, and improve infrastructure planning. Three clustering algorithms were evaluated, K-shape, DBSCAN, and DTW with K-means, to determine which one best characterizes the load curves of transformers. The results show that DTW with K-means provides the best segmentation, with a cross-correlation similarity of 0.9552 and a temporal consistency index of 0.9642. For predictive modeling, supervised algorithms were tested, where Random Forest achieved the highest accuracy in predicting the corresponding load curve type for each transformer (0.78), and the SVR model provided the best performance in predicting the maximum load, explaining 90% of the load variability (R2 = 0.90). The models were applied to 16,696 transformers in the Ecuadorian electrical sector, validating the load prediction with an accuracy of 98.55%. Additionally, the optimized assignment of the transformers’ nominal power reduced installed capacity by 39.27%, increasing the transformers’ utilization factor from 31.79% to 52.35%. These findings highlight the value of data-driven approaches for optimizing electrical distribution systems.
000153224 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000153224 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000153224 700__ $$aLópez-Eugenio, Kevin
000153224 700__ $$aDel-Valle-Soto, Carolina
000153224 700__ $$0(orcid)0000-0002-9408-1280$$aPalacios-Navarro, Guillermo$$uUniversidad de Zaragoza
000153224 700__ $$aVarela-Aldás, José
000153224 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica
000153224 773__ $$g18, 7 (2025), 1832 [24 pp.]$$pENERGIES$$tEnergies$$x1996-1073
000153224 8564_ $$s6717102$$uhttps://zaguan.unizar.es/record/153224/files/texto_completo.pdf$$yVersión publicada
000153224 8564_ $$s2448759$$uhttps://zaguan.unizar.es/record/153224/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000153224 909CO $$ooai:zaguan.unizar.es:153224$$particulos$$pdriver
000153224 951__ $$a2025-10-17-14:35:43
000153224 980__ $$aARTICLE