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

arXiv: https://arxiv.org/abs/2106.01021 · HTML: https://mlsanity.com/paper/2106.01021/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 40 references that are in the map.
- First posted on arXiv 2 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [On Distributed Quantization for Classification](https://mlsanity.com/paper/1911.00216/) (2019)
- [Shape-Based Approach to Household Load Curve Clustering and Prediction](https://mlsanity.com/paper/1702.01414/) (2017)
- [Reducing the Impact of EV Charging Operations on the Distribution Network](https://mlsanity.com/paper/1509.07056/) (2015)
- [Impact of different time series aggregation methods on optimal energy system design](https://mlsanity.com/paper/1708.00420/) (2017)
- [An Efficient Robust Solution to the Two-Stage Stochastic Unit Commitment Problem](https://mlsanity.com/paper/1606.06014/) (2016)
- [Decision-Oriented Communications: Application to Energy-Efficient Resource Allocation](https://mlsanity.com/paper/1905.07339/) (2019)

## Built on by (most cited first, 4 of 4)

- [Goal-Oriented Quantization: Analysis, Design, and Application to Resource Allocation](https://mlsanity.com/paper/2209.15347/) (2022)
- [Towards Improving Unit Commitment Economics: An Add-On Tailor for Renewable Energy and Reserve Predictions](https://mlsanity.com/paper/2208.13065/) (2022)
- [Multiple Dynamic Pricing for Demand Response with Adaptive Clustering-based Customer Segmentation in Smart Grids](https://mlsanity.com/paper/2106.05905/) (2021)
- [Strategic Federated Learning: Application to Smart Meter Data Clustering](https://mlsanity.com/paper/2408.02384/) (2024)

## Abstract (arXiv)

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.