A clustering solution for analyzing residential water consumption patterns

نویسندگان

چکیده

Water utility companies in urban areas face two major challenges: ensuring there is enough water for everyone during prolonged drought and maintaining adequate pressure the hours of peak demand. These issues can be overcome by applying data analytics machine learning to gathered from digital meters. For conservation demand management strategies effective, need gain a better understanding consumer behaviours, habits routines. To accomplish this goal, we adapted clustering approach reveal residential consumption patterns within metered data. In experiment, used sets (engineered features set as well times use weighted probabilities set) based on collected over 10 months 306 households Melbourne, Australia. engineered set, first, identified number optimal clusters. We then performed extensive experiments find best terms performance evaluation quality. chose hierarchical agglomerative technique nature objective study. observed that k-means performing after considering metrics. other found clusters varies type water-consumption event, day (i.e., weekday or weekend), profiling interval probability use. addition, insight into tap-water usage could determine population’s adaptation hygiene practices an unprecedented time, such COVID-19 pandemic. Finally, recommend future studies also employ aligned socio-demographic key features. • Discussed detailed methodology analyse clustering. Performed comprehensive experimental study methods. Identified most suitable similarity among items each cluster. Observed how hand-hygiene like scenarios.

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ژورنال

عنوان ژورنال: Knowledge Based Systems

سال: 2021

ISSN: ['1872-7409', '0950-7051']

DOI: https://doi.org/10.1016/j.knosys.2021.107522