A deep generative model for probabilistic energy forecasting in power systems: normalizing flows

• Published in: Applied energy Vol. 305; p. 117871
• Main Authors: Dumas, Jonathan, Wehenkel, Antoine, Lanaspeze, Damien, Cornélusse, Bertrand, Sutera, Antonio
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2022
• Subjects: case studies; decision making; Deep learning; electricity; energy; Energy forecasting; Generative adversarial networks; Normalizing flows; solar energy; Time series; uncertainty; Variational autoencoders; wind; Deep learning; Variational autoencoders; Generative adversarial networks; Normalizing flows; Energy forecasting; Time series

Greater direct electrification of end-use sectors with a higher share of renewables is one of the pillars to power a carbon-neutral society by 2050. However, in contrast to conventional power plants, renewable energy is subject to uncertainty raising challenges for their interaction with power syste...