Deep Generative Methods for Producing Forecast Trajectories in Power
  Systems
Nathan Weill Jonathan Dumas
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 \textit{ad hoc}
evaluation metrics for time series generation.