Denoising diffusion probabilistic models for probabilistic energy forecasting

Scenario-based probabilistic forecasts have become a vital tool to equip decision-makers to address the uncertain nature of renewable energies. This paper presents a recent promising deep learning generative approach: denoising diffusion probabilistic models. It is a class of latent variable models that have recently demonstrated impressive results in the computer vision community. However, to the best of our knowledge, there has yet to be a demonstration that they can generate high-quality samples of load, PV, or wind power time series that are crucial 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 that this approach is competitive with other state-of-the-art deep learning generative models: generative adversarial networks, variational autoencoders, and normalizing flows.