Continuous intraday electricity markets play an increasingly important role in short-term trading and balancing, yet decision-making under rapidly evolving price dynamics remains challenging. This paper proposes a comprehensive framework for ensemble forecasting of intraday electricity price trajectories and their translation into adaptive trading decisions. Building on a corrected Support Vector Regression model, the approach extends point predictions to probabilistic trajectory forecasts by introducing scenario generation based on forecast errors of fundamental variables and proposing a novel Support Vector Sorting procedure for the efficient selection of representative scenarios. The framework is evaluated using transaction level data from the German intraday continuous market. Empirical results show improvements over benchmark methods in both statistical and economic terms. Fundamental scenarios enhance median trajectory accuracy but produce more concentrated predictive distributions, while historical simulation with scenario selection better captures tail risk. From an economic perspective, ensemble-based forecasts outperform naive benchmarks across most of the trading strategies. Dynamic updating through scenario reweighting further improves profitability with limited impact on downside risk. Overall, the results demonstrate that combining kernel-based learning with scenario driven uncertainty and adaptive updating provides a flexible and effective approach for forecasting and trading in continuous electricity markets.

翻译：连续日内电力市场在短期交易和平衡中扮演着日益重要的角色，但在价格动态快速演变的背景下进行决策仍具挑战性。本文提出一个综合框架，用于日内电价轨迹的集合预测及其向自适应交易决策的转化。该方法基于修正支持向量回归模型，通过引入基于基础变量预测误差的情景生成，将点预测扩展至概率轨迹预测，并提出创新的支持向量排序过程，以高效选择代表性情景。该框架采用德国日内连续市场的交易级数据进行评估。实证结果显示，该方法在统计和经济指标上均优于基准方法。基础情景可提升中位轨迹精度，但会产生更集中的预测分布；而结合情景选择的历史模拟则能更好地捕捉尾部风险。从经济视角看，基于集合的预测在多数交易策略中优于朴素基准。通过情景重新加权实现的动态更新可进一步提升盈利能力，且对下行风险影响有限。总体而言，研究结果表明，将基于核的学习方法、情景驱动的不确定性建模与自适应更新相结合，为连续电力市场中的预测与交易提供了一种灵活有效的解决方案。