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A Deep Generative Model for Probabilistic Energy Forecasting in Power Systems: Normalizing Flows by Dumas, Jonathan; Lanaspeze, Antoine Wehenkel Damien; Cornélusse, Bertrand; Sutera, Antonio is a scholarly article available to read on EtoBox.

What is A Deep Generative Model for Probabilistic Energy Forecasting in Power Systems: Normalizing Flows about?

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 systems. Scenario-based probabilistic forecasting models have become a vital tool to equip decision-makers. This paper presents to the power systems forecasting practitioners a recent deep learning technique, the normalizing flows, to produce accurate scenario-based probabilistic forecasts that are crucial to face the new challenges in power systems applications. The strength of this technique is to directly learn the stochastic multivariate distribution of the underlying process by maximizing the likelihood. Through comprehensive empirical evaluations using the open data of the Global Energy Forecasting Competition 2014, we demonstrate that this methodology is competitive with other state-of-the-art deep learning generative models: generative adversarial networks and variational autoencoders. The models producing weather-based wind, solar power, and load scenarios are properly compared in te

Author

Dumas, Jonathan; Lanaspeze, Antoine Wehenkel Damien; Cornélusse, Bertrand; Sutera, Antonio

Published

2021

Language

EN