  Data clustering is an instrumental tool in the area of energy resource
management. One problem with conventional clustering is that it does not take
the final use of the clustered data into account, which may lead to a very
suboptimal use of energy or computational resources. When clustered data are
used by a decision-making entity, it turns out that significant gains can be
obtained by tailoring the clustering scheme to the final task performed by the
decision-making entity. The key to having good final performance is to
automatically extract the important attributes of the data space that are
inherently relevant to the subsequent decision-making entity, and partition the
data space based on these attributes instead of partitioning the data space
based on predefined conventional metrics. For this purpose, we formulate the
framework of decision-making oriented clustering and propose an algorithm
providing a decision-based partition of the data space and good representative
decisions. By applying this novel framework and algorithm to a typical problem
of real-time pricing and that of power consumption scheduling, we obtain
several insightful analytical results such as the expression of the best
representative price profiles for real-time pricing and a very significant
reduction in terms of required clusters to perform power consumption scheduling
as shown by our simulations.
