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- W4310921346 abstract "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." @default.
- W4310921346 created "2022-12-21" @default.
- W4310921346 creator A5030367603 @default.
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- W4310921346 date "2022-12-06" @default.
- W4310921346 modified "2023-09-26" @default.
- W4310921346 title "Denoising diffusion probabilistic models for probabilistic energy forecasting" @default.
- W4310921346 doi "https://doi.org/10.1109/powertech55446.2023.10202713" @default.
- W4310921346 hasPublicationYear "2022" @default.
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