mlsanityData to 14 Oct 2026

Deep Generative Methods for Producing Forecast Trajectories in Power Systems

arXiv 2309.15137 ↗First posted 26 Sep 2023cs.LG cs.AIcited by 0

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Abstract (arXiv)

With the expansion of renewables in the electricity mix, power grid variability will increase, hence a need to robustify the system to guarantee its security. Therefore, Transport System Operators (TSOs) must conduct analyses to simulate the future functioning of power systems. Then, these simulations are used as inputs in decision-making processes. In this context, we investigate using deep learning models to generate energy production and load forecast trajectories. To capture the spatiotemporal correlations in these multivariate time series, we adapt autoregressive networks and normalizing flows, demonstrating their effectiveness against the current copula-based statistical approach. We conduct extensive experiments on the French TSO RTE wind forecast data and compare the different models with textitad hoc evaluation metrics for time series generation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 36 references that are in the map. First posted on arXiv 26 Sep 2023.

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22 of 36 references.

References · 22

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10 more
  1. 2017GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibriumcited by 213
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