Scenario-based probabilistic forecasts have become vital for decision-makers in handling intermittent renewable energies. This paper presents a recent promising deep learning generative approach called denoising diffusion probabilistic models. It is a class of latent variable models which have recently demonstrated impressive results in the computer vision community. However, to our knowledge, there has yet to be a demonstration that they can generate high-quality samples of load, PV, or wind power time series, crucial elements to face the new challenges in power systems applications. Thus, we propose the first implementation of this model for energy forecasting using the open data of the Global Energy Forecasting Competition 2014. The results demonstrate this approach is competitive with other state-of-the-art deep learning generative models, including generative adversarial networks, variational autoencoders, and normalizing flows.

翻译：基于场景的概率预测已成为决策者应对间歇性可再生能源问题的关键工具。本文提出了一种近期极具潜力的深度学习生成方法——去噪扩散概率模型。该模型属于隐变量模型类别，近期在计算机视觉领域展现了令人瞩目的成果。然而，据我们所知，目前尚未有研究验证其能否生成负荷、光伏或风电时间序列的高质量样本，而这些样本正是应对电力系统应用新挑战的核心要素。为此，我们首次基于2014年全球能源预测竞赛的开放数据，将该模型应用于能源预测。结果表明，该方法与包括生成对抗网络、变分自编码器及归一化流在内的其他前沿深度学习生成模型相比具有竞争性。