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Enhancing Load Stratification in Power Distribution Systems Through Clustering Algorithms: A Practical Study

Williams Mendoza-Vitonera, Xavier Serrano-Guerrero (), María-Fernanda Cabrera, John Enriquez-Loja () and Antonio Barragán-Escandón
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Williams Mendoza-Vitonera: Energy Transition Research Group (GITE), Universidad Politécnica Salesiana, Calle Vieja 12-30 y Elia Liut, Cuenca 010102, Ecuador
Xavier Serrano-Guerrero: Energy Transition Research Group (GITE), Universidad Politécnica Salesiana, Calle Vieja 12-30 y Elia Liut, Cuenca 010102, Ecuador
María-Fernanda Cabrera: Energy Transition Research Group (GITE), Universidad Politécnica Salesiana, Calle Vieja 12-30 y Elia Liut, Cuenca 010102, Ecuador
John Enriquez-Loja: Energy Transition Research Group (GITE), Universidad Politécnica Salesiana, Calle Vieja 12-30 y Elia Liut, Cuenca 010102, Ecuador
Antonio Barragán-Escandón: Department of Electrical Engineering, Electronics and Telecommunications, Universidad de Cuenca, Cuenca 010107, Ecuador

Energies, 2025, vol. 18, issue 19, 1-19

Abstract: Accurate load profile identification is crucial for effective and sustainable power system planning. This study proposes a characterization methodology based on clustering techniques applied to consumption data from medium- and low-voltage users, as well as distribution transformers from an electric utility. Three algorithms—K-means, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), and Gaussian Mixture Models (GMM)—were implemented and compared in terms of their ability to form representative strata using variables such as observation count, projected energy, load factor ( L F ), and characteristic power levels. The methodology includes data cleaning, normalization, dimensionality reduction, and quality metric analysis to ensure cluster consistency. Results were benchmarked against a prior study conducted by Empresa Eléctrica Regional Centro Sur C.A. (EERCS). Among the evaluated algorithms, GMM demonstrated superior performance in modeling irregular consumption patterns and probabilistically assigning observations, resulting in more coherent and representative segmentations. The resulting clusters exhibited an average L F of 58.82%, indicating balanced demand distribution and operational consistency across the groups. Compared to alternative clustering techniques, GMM demonstrated advantages in capturing heterogeneous consumption patterns, adapting to irregular load behaviors, and identifying emerging user segments such as induction-cooking households. These characteristics arise from its probabilistic nature, which provides greater flexibility in cluster formation and robustness in the presence of variability. Therefore, the findings highlight the suitability of GMM for real-world applications where representativeness, efficiency, and cluster stability are essential. The proposed methodology supports improved transformer sizing, more precise technical loss assessments, and better demand forecasting. Periodic application and integration with predictive models and smart grid technologies are recommended to enhance strategic and operational decision-making, ultimately supporting the transition toward smarter and more resilient power distribution systems.

Keywords: cluster; daily load profiles; load stratification; strata (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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