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Elaborating demand side management strategies is crucial for integrating electricity from renewable sources into the electrical grid. Though future demand side will largely depend on an automatic control of larger loads, it is also widely agreed upon that consumer behavior will play an important role as well - be it by purchasing respective automation techniques or by shifting the use of appliances to other times of the day. Doing so, it becomes possible to select households that offer sufficient load shifting potential, and to overcome undirected and thus, expensive campaigns. To our knowledge, this perspective is still under-researched, especially when it comes to clustering methods on load consumption data with a focus on peak detection accuracy to provide customer segmentation. Using the data collected in the Irish CER dataset, which contains readings for more than 4000 residential customers over a period of 18 months at 30-minute intervals, we show that the whole clustering of the time series, with a few adaptations on the usage of the K-Means algorithm, provides better clustering results without sacrificing practical feasibility. Characteristic load profiles allow us to segment the customers, address groups of households with similar consumption patterns and determine on the fly the cluster membership of a given load curve. This will support decision making regarding the investments in load shifting campaigns to prevent over or under-dimensioning linked to peak energy demand.
Cao et al. (Fri,) studied this question.