The paper presents a performance assessment of load profiles clustering methods using silhouette value criterion, a method of interpretation and validation of consistency within clusters of data. Three of the most common clustering methods are considered: Density-Based Spatial Clustering of Applications with Noise, Hierarchical cluster analysis, k-means clustering. Based on a large dataset of real medium voltage (MV) substation load profiles, the approaches have been first assessed on multiple smaller subsets of randomly selected data. Different representations of the data are considered in terms of temporal resolution and data scaling varying clustering parameters in a sort of sensitivity analysis. Based on average silhouette values, the clustering methods are ranked. The approaches are then applied to the entire dataset and, based on the clusters identified, some standard load profiles are extracted, shown, and briefly discuss.

Performance assessment of load profiles clustering methods based on silhouette analysis / Bosisio, Alessandro; Berizzi, Alberto; Morotti, Andrea; Greco, Bartolomeo; Iannarelli, Gaetano; Moscatiello, Cristina; Boccaletti, Chiara; Noriega, Holguer. - (2021), pp. 1-6. (Intervento presentato al convegno 2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) tenutosi a Bari, Italy) [10.1109/EEEIC/ICPSEurope51590.2021.9584629].

Performance assessment of load profiles clustering methods based on silhouette analysis

Greco, Bartolomeo;Iannarelli, Gaetano;Moscatiello, Cristina;Boccaletti, Chiara;
2021

Abstract

The paper presents a performance assessment of load profiles clustering methods using silhouette value criterion, a method of interpretation and validation of consistency within clusters of data. Three of the most common clustering methods are considered: Density-Based Spatial Clustering of Applications with Noise, Hierarchical cluster analysis, k-means clustering. Based on a large dataset of real medium voltage (MV) substation load profiles, the approaches have been first assessed on multiple smaller subsets of randomly selected data. Different representations of the data are considered in terms of temporal resolution and data scaling varying clustering parameters in a sort of sensitivity analysis. Based on average silhouette values, the clustering methods are ranked. The approaches are then applied to the entire dataset and, based on the clusters identified, some standard load profiles are extracted, shown, and briefly discuss.
2021
2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
substations; power demand; sensitivity analysis; clustering methods; clustering algorithms; medium voltage; market research
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Performance assessment of load profiles clustering methods based on silhouette analysis / Bosisio, Alessandro; Berizzi, Alberto; Morotti, Andrea; Greco, Bartolomeo; Iannarelli, Gaetano; Moscatiello, Cristina; Boccaletti, Chiara; Noriega, Holguer. - (2021), pp. 1-6. (Intervento presentato al convegno 2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) tenutosi a Bari, Italy) [10.1109/EEEIC/ICPSEurope51590.2021.9584629].
File allegati a questo prodotto
File Dimensione Formato  
Bosisio_Performance_2021.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 728.78 kB
Formato Adobe PDF
728.78 kB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1615546
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 0
social impact