Are domestic load profiles stable over time? An attempt to identify target households for demand side management campaigns

Abstract

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.

Evaluation of the performance of clustering algorithms for a high voltage industrial consumer

“…Partitional clustering algorithms, like the K-means, Weighted Fuzzy Average (WFA) K-means, Improved Weight Fuzzy Average (IWFA) K-means and modified "follow-the-leader" (Tsekouras et al, 2007;Chicco et al, 2004;Bidaki et al, 2010;Kohan et al, 2009;Cao et al, 2013;Ramos et al, 2013;Kwac et al, 2014).…”

“…Low voltage consumers (Räsänen et al, 2010;Figueiredo et al, 2005;López et al, 2011;Rodrigues et al, 2003;Nizar et al, 2006, Anuar andZakaria, 2012;Hino et al, 2013;Cao et al, 2013;Iglesias and Kastner, 2013;Piao et al, 2014).…”

Revealing household characteristics from smart meter data

“…Here, we distinguish between (1) analyzing consumption data only and (2) relating it to side-information such as the geographic location of the dwelling or the socio-economic status of the household. Since the first approach imposes less requirements on the collected data, many authors have investigated unsupervised techniques such as clustering to detect patterns and usage categories in the consumption profiles [17,18,19,20]. Chicco, for instance, provides an overview of clustering techniques used to group residential or commercial customers according to their electricity consumption pattern [19].…”

“…Grouping consumers by their load profile enables utilities to formulate tariffs for specific customer categories, check the effect of tariff modifications, and ultimately optimize their supply management. Using similar techniques, both Kwac et al [18] and Cao et al [17] have focused identifying the "right" customers for demand-side management campaigns. Whereas Kwac et al aim at detecting stable profiles over a certain time period, Cao et al focus on identifying households with a similar time of peak usage.…”

End-use Appliance Control Technique for Domestic Peak Load Management

“…In such case, a cost effective solution is domestic load control with optimal management of the present generating capabilities of the power utility in order to minimize demand and energy consumption in households without compromising consumers' needs during peak periods. Occupant's consumption behaviour varies vastly and do impact on energy consumption in a home [7][8][9][10]. Hence the need for an investigation on domestic occupant's energy consumption behaviour for enhanced peak load management modelling.…”