# Estimating Demand Flexibility Using Siamese LSTM Neural Networks

arXiv: https://arxiv.org/abs/2109.01258 · HTML: https://mlsanity.com/paper/2109.01258/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 47 references that are in the map.
- First posted on arXiv 3 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation](https://mlsanity.com/paper/1712.00080/) (2017)
- [A Data-driven Bidding Model for a Cluster of Price-responsive Consumers of Electricity](https://mlsanity.com/paper/1506.06587/) (2015)
- [Review of Learning-Assisted Power System Optimization](https://mlsanity.com/paper/2007.00210/) (2020)

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

- [Massively Digitized Power Grid: Opportunities and Challenges of Use-inspired AI](https://mlsanity.com/paper/2205.05180/) (2022)
- [Supporting Future Electrical Utilities: Using Deep Learning Methods in EMS and DMS Algorithms](https://mlsanity.com/paper/2303.00428/) (2023)
- [Estimating the Unobservable Components of Electricity Demand Response with Inverse Optimization](https://mlsanity.com/paper/2410.02774/) (2024)

## Abstract (arXiv)

There is an opportunity in modern power systems to explore the demand flexibility by incentivizing consumers with dynamic prices. In this paper, we quantify demand flexibility using an efficient tool called time-varying elasticity, whose value may change depending on the prices and decision dynamics. This tool is particularly useful for evaluating the demand response potential and system reliability. Recent empirical evidences have highlighted some abnormal features when studying demand flexibility, such as delayed responses and vanishing elasticities after price spikes. Existing methods fail to capture these complicated features because they heavily rely on some predefined (often over-simplified) regression expressions. Instead, this paper proposes a model-free methodology to automatically and accurately derive the optimal estimation pattern. We further develop a two-stage estimation process with Siamese long short-term memory (LSTM) networks. Here, a LSTM network encodes the price response, while the other network estimates the time-varying elasticities. In the case study, the proposed framework and models are validated to achieve higher overall estimation accuracy and better description for various abnormal features when compared with the state-of-the-art methods.