mlsanityData to 14 Oct 2026

The day-ahead scenario generation method for new energy based on an improved conditional generative diffusion model

arXiv 2503.07648 ↗First posted 6 Mar 2025cs.LGcited by 0

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

In the context of the rising share of new energy generation, accurately generating new energy output scenarios is crucial for day-ahead power system scheduling. Deep learning-based scenario generation methods can address this need, but their black-box nature raises concerns about interpretability. To tackle this issue, this paper introduces a method for day-ahead new energy scenario generation based on an improved conditional generative diffusion model. This method is built on the theoretical framework of Markov chains and variational inference. It first transforms historical data into pure noise through a diffusion process, then uses conditional information to guide the denoising process, ultimately generating scenarios that satisfy the conditional distribution. Additionally, the noise table is improved to a cosine form, enhancing the quality of the generated scenarios. When applied to actual wind and solar output data, the results demonstrate that this method effectively generates new energy output scenarios with good adaptability.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 4 of its 12 references that are in the map. First posted on arXiv 6 Mar 2025.

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4 of 12 references.

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  1. 2023Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting21 Jul

Other references · 3

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2022Classifier-Free Diffusion Guidancecited by 5,847
  3. 2017Model-Free Renewable Scenario Generation Using Generative Adversarial Networkscited by 51

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