# Generative Modeling and Data Augmentation for Power System Production Simulation

arXiv: https://arxiv.org/abs/2412.12146 · HTML: https://mlsanity.com/paper/2412.12146/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 10 Dec 2024.

## Builds on (most cited first, 2 of 2)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection](https://mlsanity.com/paper/2103.05094/) (2021)

## Built on by (most cited first, 1 of 1)

- [Diffusion-assisted Model Predictive Control Optimization for Power System Real-Time Operation](https://mlsanity.com/paper/2505.08535/) (2025)

## Abstract (arXiv)

As a key component of power system production simulation, load forecasting is critical for the stable operation of power systems. Machine learning methods prevail in this field. However, the limited training data can be a challenge. This paper proposes a generative model-assisted approach for load forecasting under small sample scenarios, consisting of two steps: expanding the dataset using a diffusion-based generative model and then training various machine learning regressors on the augmented dataset to identify the best performer. The expanded dataset significantly reduces forecasting errors compared to the original dataset, and the diffusion model outperforms the generative adversarial model by achieving about 200 times smaller errors and better alignment in latent data distributions.