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Generative Modeling and Data Augmentation for Power System Production Simulation

Linna Xu, Yongli Zhu

TL;DR

The paper tackles load forecasting under small-sample data in power-system production simulation and proposes a diffusion-based time-series augmentation approach (TS-Diffusion) to generate high-quality training data. It compares TS-Diffusion with TimeGAN, showing TS-Diffusion yields substantially lower forecasting errors and better latent-distribution alignment, thereby improving predictive accuracy when training multiple regressors. A simple production-simulation demonstration using forecasts from the augmented data (via an ExtraTree model) illustrates practical benefits, including PV-driven operation and reduced grid purchases under a cost-minimization objective. Overall, the work demonstrates that diffusion-based data augmentation can significantly enhance forecasting accuracy and downstream operational planning in data-scarce power systems; future work includes transfer-learning to improve cross-region generalization. The optimization considered can be summarized as minimizing $cost_{grid}\sum_{t=1}^{T} P_{grid}(t) + cost_{pv}\sum_{t=1}^{T} P_{pv}(t)$ subject to $P_{load}(t)=P_{grid}(t)+P_{pv}(t)$ and $0\le P_{pv}(t) \le P_{pv,max}(t)$, illustrating how forecast quality directly affects economic outcomes.

Abstract

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.

Generative Modeling and Data Augmentation for Power System Production Simulation

TL;DR

The paper tackles load forecasting under small-sample data in power-system production simulation and proposes a diffusion-based time-series augmentation approach (TS-Diffusion) to generate high-quality training data. It compares TS-Diffusion with TimeGAN, showing TS-Diffusion yields substantially lower forecasting errors and better latent-distribution alignment, thereby improving predictive accuracy when training multiple regressors. A simple production-simulation demonstration using forecasts from the augmented data (via an ExtraTree model) illustrates practical benefits, including PV-driven operation and reduced grid purchases under a cost-minimization objective. Overall, the work demonstrates that diffusion-based data augmentation can significantly enhance forecasting accuracy and downstream operational planning in data-scarce power systems; future work includes transfer-learning to improve cross-region generalization. The optimization considered can be summarized as minimizing subject to and , illustrating how forecast quality directly affects economic outcomes.

Abstract

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.

Paper Structure

This paper contains 14 sections, 7 equations, 7 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Dataset Augmentation for Load Forecasting
  3. Dataset Description
  4. TS-Diffusion Model for Load Demand Augmentation
  5. Experiment Results
  6. Quality of TS-Diffusion Augmented Dataset
  7. Quality of TimeGAN Augmented Dataset
  8. A Simple Showcase for Power System Production Simulation
  9. Conclusion and Future Work
  10. Appendix
  11. GAN-based Load Demand Augmentation Model
  12. TS-Diffusion-based Load Demand Augmentation Model
  13. ExtraTree-based Load Forecasting Model
  14. An In-depth Analysis of the Experiment Results

Figures (7)

  • Figure 1: TS-Diffusion training framework.
  • Figure 2: (a) a regional power grid; (b) production simulation results
  • Figure 3: TimeGAN training framework.
  • Figure 4: ExtraTree Model.
  • Figure 5: PCA of the TS-Diffusion generated data and the original data (right) and PCA of the TimeGAN generated data and the original data (left).
  • ...and 2 more figures