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.

翻译：电力价格预测（EPF）作为交易决策或能源管理运营的基础输入，对电力公司具有重要作用。由于电力无法储存，电价具有高度波动性，这使得EPF成为一项尤为困难的任务。当重大突发事件扰乱市场时，这一挑战更为严峻。交易及更广义的能源管理决策需要基于概率EPF（PEPF）的风险管理工具。在此充满挑战的背景下，我们主张部署高度自适应的黑箱策略，例如共形预测和在线聚合，作为任何操作流程中关键的最后层，能够将任意预测转化为稳健的自适应预测区间。我们提出研究一个包含2020-2021年动荡时期法国电力现货价格的新数据集，并构建了一个具有高预测能力的新解释特征——核能可用性。在该数据集上对最先进的PEPF方法进行基准测试表明，选择特定模型十分困难，因为它们在实践中的表现差异巨大，且无一可靠。然而，我们提出了一种恰当的共形化方法OSSCP-horizon，即使在2021年末最动荡的时期，也能提升PEPF方法的性能。最后，我们强调将其与在线聚合相结合可显著超越其他所有方法，应作为首选流程，因为它能提供可信的概率预测。