Generative Modeling and Data Augmentation for Power System Production Simulation
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917870390214656 |
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| author | Xu, Linna Zhu, Yongli |
| author_facet | Xu, Linna Zhu, Yongli |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_12146 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative Modeling and Data Augmentation for Power System Production Simulation Xu, Linna Zhu, Yongli Systems and Control Artificial Intelligence Machine Learning 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. |
| title | Generative Modeling and Data Augmentation for Power System Production Simulation |
| topic | Systems and Control Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2412.12146 |