Denoising diffusion probabilistic models for probabilistic energy forecasting
This provides a new method for energy forecasting to help decision-makers handle intermittent renewables, but it is incremental as it applies an existing technique to a new domain.
The paper tackles the problem of generating high-quality probabilistic forecasts for renewable energy time series (load, PV, wind power) by implementing denoising diffusion probabilistic models, a deep learning generative approach, and shows it is competitive with state-of-the-art models like GANs, VAEs, and normalizing flows.
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.