Comparisons among clustering techniques for electricity customer classification

By Gianfranco Chicco, Roberto Napoli, Federico Piglione. IEEE Transactions on Power Systems, 2006

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Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks (2013, methodology): “For that reason, CCA has been applied in different fields: initially, to pattern recognition [27], after, in electrical power systems [28], and, finally, in the electromechanical sys-”
Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns (2013, background): “As a general rule, in this scope clustering is commonly used to classify energy consumers [7], predict future energy demand [8,9], or detect distinguished, habitually undesired, behaviors ( i.e. , outliers) [10].”
Household Energy Consumption Segmentation Using Hourly Data (2014, methodology): “Some significant contributions in segmentation are [3], [4], [16], [17], [19], [20].”
Analysis and Clustering of Residential Customers Energy Behavioral Demand Using Smart Meter Data (2016, methodology): “IN POWER systems, clustering methods have traditionally been applied to energy data of high/medium voltage customers or on larger aggregations of customers [1], [2].”