  Electricity price forecasting (EPF) plays a major role for electricity
companies as a fundamental entry for trading decisions or energy management
operations. As electricity can not be stored, electricity prices are highly
volatile which make EPF a particularly difficult task. This is all the more
true when dramatic fortuitous events disrupt the markets. Trading and more
generally energy management decisions require risk management tools which are
based on probabilistic EPF (PEPF). In this challenging context, we argue in
favor of the deployment of highly adaptive black-boxes strategies allowing to
turn any forecasts into a robust adaptive predictive interval, such as
conformal prediction and online aggregation, as a fundamental last layer of any
operational pipeline.
  We propose to investigate a novel data set containing the French electricity
spot prices during the turbulent 2020-2021 years, and build a new explanatory
feature revealing high predictive power, namely the nuclear availability.
Benchmarking state-of-the-art PEPF on this data set highlights the difficulty
of choosing a given model, as they all behave very differently in practice, and
none of them is reliable. However, we propose an adequate conformalisation,
OSSCP-horizon, that improves the performances of PEPF methods, even in the most
hazardous period of late 2021. Finally, we emphasize that combining it with
online aggregation significantly outperforms any other approaches, and should
be the preferred pipeline, as it provides trustworthy probabilistic forecasts.
