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Data clustering is an instrumental tool in the area of energy resourcemanagement. One problem with conventional clustering is that it does not takethe final use of the clustered data into account, which may lead to a verysuboptimal use of energy or computational resources. When clustered data areused by a decision-making entity, it turns out that significant gains can beobtained by tailoring the clustering scheme to the final task performed by thedecision-making entity. The key to having good final performance is toautomatically extract the important attributes of the data space that areinherently relevant to the subsequent decision-making entity, and partition thedata space based on these attributes instead of partitioning the data spacebased on predefined conventional metrics. For this purpose, we formulate theframework of decision-making oriented clustering and propose an algorithmproviding a decision-based partition of the data space and good representativedecisions. By applying this novel framework and algorithm to a typical problemof real-time pricing and that of power consumption scheduling, we obtainseveral insightful analytical results such as the expression of the bestrepresentative price profiles for real-time pricing and a very significantreduction in terms of required clusters to perform power consumption schedulingas shown by our simulations.

Decision-making Oriented Clustering: Application to Pricing and Power Consumption Scheduling