# Learning the LMP-Load Coupling From Data: A Support Vector Machine Based Approach

arXiv: https://arxiv.org/abs/1603.07276 · HTML: https://mlsanity.com/paper/1603.07276/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2016.

## Built on by (most cited first, 3 of 3)

- [Graph Neural Networks for Learning Real-Time Prices in Electricity Market](https://mlsanity.com/paper/2106.10529/) (2021)
- [Unsupervised Congestion Status Identification Using LMP Data](https://mlsanity.com/paper/2411.10058/) (2024)
- [A Data-Driven Pool Strategy for Price-Makers Under Imperfect Information](https://mlsanity.com/paper/2411.14694/) (2024)

## Abstract (arXiv)

This paper investigates the fundamental coupling between loads and locational marginal prices (LMPs) in security-constrained economic dispatch (SCED). Theoretical analysis based on multi-parametric programming theory points out the unique one-to-one mapping between load and LMP vectors. Such one-to-one mapping is depicted by the concept of system pattern region (SPR) and identifying SPRs is the key to understanding the LMP-load coupling. Built upon the characteristics of SPRs, the SPR identification problem is modeled as a classification problem from a market participant's viewpoint, and a Support Vector Machine based data-driven approach is proposed. It is shown that even without the knowledge of system topology and parameters, the SPRs can be estimated by learning from historical load and price data. Visualization and illustration of the proposed data-driven approach are performed on a 3-bus system as well as the IEEE 118-bus system.