In the present paper, different clustering techniques were applied to detect significant patterns describing single-household water consumption in a residential neighborhood of the City of Naples, basing on hourly time series aggregated at the monthly scale. Comparisons among results were performed by means of a selection of Clustering Validity Indices, that were adjusted to overcome a bias caused by sparsely populated clusters. The most performant cluster solution proved to be the one resulting from the application of a mixed strategy, namely a Self-Organized Map followed by K-means performed on first level cluster centroids.
Identification of Annual Water Demand Patterns in the City of Naples / Padulano, Roberta; Giudice, Giuseppe Del; Giugni, Maurizio; Fontana, Nicola; Uberti, Gianluca Sorgenti Degli. - In: PROCEEDINGS. - ISSN 2504-3900. - 2:11(2018), p. 587. [10.3390/proceedings2110587]
Identification of Annual Water Demand Patterns in the City of Naples
Padulano, Roberta;Giudice, Giuseppe Del;Giugni, Maurizio;
2018
Abstract
In the present paper, different clustering techniques were applied to detect significant patterns describing single-household water consumption in a residential neighborhood of the City of Naples, basing on hourly time series aggregated at the monthly scale. Comparisons among results were performed by means of a selection of Clustering Validity Indices, that were adjusted to overcome a bias caused by sparsely populated clusters. The most performant cluster solution proved to be the one resulting from the application of a mixed strategy, namely a Self-Organized Map followed by K-means performed on first level cluster centroids.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.