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

TL;DR

This paper introduces a decision-making oriented clustering framework that tailors data partitioning to specific decision tasks, improving energy management and scheduling efficiency.

Contribution

It proposes a novel clustering algorithm that extracts decision-relevant attributes, enhancing performance in real-time pricing and power consumption scheduling.

Findings

Optimal representative price profiles derived

Significant reduction in clusters needed for scheduling

Enhanced energy resource management efficiency

Abstract

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.