Electricity price signals in modern power systems exhibit complex dependence structures that render forecasting inherently challenging. Our analysis of real-world pricing signals from the California Independent System Operator (CAISO) reveals complex temporal group effects, whereby the influence of explanatory variables on electricity prices persists across consecutive blocks of time due to underlying economic and operational drivers. In response, we propose a multivariate statistical method based on a Group Lasso formulation to forecast the vector of day-ahead electricity prices, by leveraging multi-feature temporal group effects. Our approach is evaluated on two full years of electricity prices from CAISO, demonstrating considerable improvements in point and probabilistic forecast metrics compared to a wide array of statistical and deep learning methods. Theoretical and empirical analyses confirm the effectiveness of the proposed approach in modeling realistic group effects, maintaining both interpretability and low computational complexity. When retrospectively evaluated on test data from a recent international electricity price forecasting challenge, the proposed method ranked in second place, despite having access to significantly less information than competing approaches. Finally, the proposed method is independently validated against two operational electricity price forecasting systems in CAISO, demonstrating competitive predictive performance and practical relevance.

翻译：现代电力系统中的电价信号呈现复杂依赖结构，使得预测极具挑战性。我们对加州独立系统运营商（CAISO）实际电价信号的分析揭示了复杂的时间组效应，即由于潜在的经济和运行驱动因素，解释变量对电价的影响会跨越连续时间区间持续存在。为此，我们提出一种基于组套索公式的多元统计方法，通过利用多特征时间组效应对日前电价向量进行预测。该方法在CAISO两年期完整电价数据上进行了评估，结果表明与多种统计及深度学习方法相比，该方法在点预测和概率预测指标上均有显著提升。理论和实证分析证实了该方法在建模实际组效应方面的有效性，同时保持了可解释性和较低的计算复杂度。在近期国际电价预测挑战赛的测试数据回溯评估中，尽管可获取的信息量远少于其他竞争方法，本方法仍位列第二。最后，该方法通过CAISO两个运行电价预测系统的独立验证，展现了具有竞争力的预测性能和实际应用价值。