Algorithms for clustering data
Algorithms for clustering data
Fundamentals of speech recognition
Fundamentals of speech recognition
A time series representation model for accurate and fast similarity detection
Pattern Recognition
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Current deregulated energy market requires that utilities have to face challenging issues that mainly arise from conceiving new customercentric frameworks instead of early supplier-centric frameworks. Enel, a large international energy utility, is able to measure and store load profiles of their mass-market low-voltage (LV) customers in a flexible and effective way thanks to the well-established Telegestore project [3, 12]. In this paper, we present a study on the characterization of LV customers based on their consumption data. A time series based model is used to suitably represent load profiles and enable the detection of their characteristic trends. Besides this primary data, we also exploit meta-data associated to the load profiles, which is useful to enrich a-priori knowledge on the customers. We conceived a clustering framework for detecting groups of customers having similar consumption behavior. We experimentally evaluated the proposed framework on a real application concerning the characterization of Enel customers according to their load profiles. Preliminary experiments have shown results which are significant in terms of clustering validity and potentially useful to practitioners from the Enel utility.