Probabilistic electricity price forecasting (PEPF) is vital for short-term electricity markets, yet the multivariate nature of day-ahead prices - spanning 24 consecutive hours - remains underexplored. At the same time, real-time decision-making requires methods that are both accurate and fast. We introduce an online algorithm for multivariate distributional regression models, allowing efficient modeling of the conditional means, variances, and dependence structures of electricity prices. The approach combines multivariate distributional regression with online coordinate descent and LASSO-type regularization (absolute shrinkage and selection operator), enabling scalable estimation in high-dimensional covariate spaces. Additionally, we propose a regularized estimation path over increasingly complex dependence structures, allowing for early stopping and avoiding overfitting. In a case study using historical data from the German day-ahead market, the proposed method yields interpretable and well-calibrated joint prediction intervals for the 24-dimensional price distribution and provides robust performance across a range of proper scoring rules. The results underscore the importance of modeling the dependence structure of electricity prices. Furthermore, we analyze the trade-off between predictive accuracy and computational costs for batch and online estimation and provide a high-performing open-source Python implementation in the ondil package.

翻译：概率电价预测（PEPF）对短期电力市场至关重要，然而日前电价的多变量特性——覆盖连续24小时——仍未得到充分探索。同时，实时决策需要兼顾准确性和快速性的方法。我们提出了一种在线多变量分布回归模型算法，能够高效建模电价的条件均值、方差及相依结构。该方法将多变量分布回归与在线坐标下降法和LASSO型正则化（绝对收缩与选择算子）相结合，实现在高维协变量空间中的可扩展估计。此外，我们提出了针对日益复杂相依结构的正则化估计路径，支持提前停止以避免过拟合。在使用德国日前市场历史数据的案例研究中，所提方法为24维价格分布生成了可解释且校准良好的联合预测区间，并在多种恰当评分规则下展现出稳健性能。研究结果强调了建模电价相依结构的重要性。此外，我们分析了批量估计与在线估计在预测精度与计算成本之间的权衡，并提供了高性能的开源Python实现（ondil包）。