论文标题
降解概率预测的扩散概率模型
Denoising diffusion probabilistic models for probabilistic energy forecasting
论文作者
论文摘要
基于方案的概率预测对于处理间歇性可再生能源的决策者至关重要。本文介绍了一种最近有希望的深度学习生成方法，称为denoising扩散概率模型。这是一类潜在变量模型，最近在计算机视觉社区中表现出了令人印象深刻的结果。但是，据我们所知，尚未有一个证明他们可以生成高质量的负载，PV或Wind Power时间序列，这是面对电源系统应用中新挑战的关键要素。因此，我们建议使用2014年全球能源预测竞赛的开放数据对该模型进行该模型进行预测。结果表明，这种方法与其他最先进的深度学习生成模型具有竞争力，包括生成的对抗网络，变异自动装码器和正常流量。
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.
