Hourly Load Curves Disaggregated by Type of Consumer Using A Density-Based Spatial Clustering Technique

Author:

Peñaloza Carlos Andrés1,Otero-Valladares Patricia Elizabeth1ORCID

Affiliation:

1. Department of Electrical Energy, Escuela Politécnica Nacional, Quito 170525, Ecuador

Publisher

MDPI

Reference6 articles.

1. (2023, March 13). Ministerio de Electricidad y Energías Naturales No Renovables, “Plan Maestro de Electricidad 2018–2027”, Chapter 3, Estudio de la Demanda. Available online: https://www.recursosyenergia.gob.ec/plan-maestro-de-electricidad/.

2. ARCONEL (2023, April 15). Plan Maestro de Electrificación 2013–2022, Perspectiva y Expansión del Sistema Eléctrico Ecuatoriano. Available online: https://www.regulacionelectrica.gob.ec/plan-maestro-de-electrificacion-2013-2022/.

3. Fong, S., and Rehman, S.U. (2014, January 17–19). DBSCAN: Past, Present and future. Proceedings of the Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014), Chennai, India.

4. Irwin, D., Albrecht, J., and Satopa, V. (2011, January 20–24). Finding a “Kneedle” in a Haystack: Detecting Knee Points in System Behavior. Proceedings of the 31st International Conference on Distributed Computing Systems Workshops, Minneapolis, MN, USA.

5. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis;Rousseeuw;J. Comput. Appl. Math.,1999

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