发表机构
Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology; Department of Computer Science, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院自动化与应用信息研究所; 卡尔斯鲁厄理工学院计算机科学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究建立了跨境电价预测的可复现评估框架，对比了六种深度学习模型在低数据场景下的表现，发现N-HiTS和NBEATSx在有限数据中表现具竞争力，Transformer模型需更多调优。
AI 中文摘要
尽管公开可用的电力市场数据为预测研究提供了宝贵资源，但该领域缺乏用于标准化比较的既定基准数据集。因此，许多研究依赖不同的数据集和指标在孤立环境中评估方法，这使得难以评估进展并一致地比较最先进的方法。在本研究中，我们使用公开数据评估深度学习模型在多个市场环境下的电价预测（EPF）表现，目标是建立一个可复现的框架，以实现对预测模型的一致评估。尽管深度学习已被用于日前电价预测，但许多先前的研究仅限于单一市场环境、狭窄的特征集或固定的训练机制。本研究对六种深度学习模型进行了比较评估，涵盖状态空间模型、多层感知机（MLP）、循环神经网络（RNN）和基于Transformer的架构，重点关注跨市场的泛化能力。我们使用零样本、单样本和少样本学习模拟低数据量的目标市场条件。我们的测试集聚焦于2024年德国-卢森堡（DE-LU）竞价区，使用包含日历、历史价格和市场衍生特征的标准化数据集。我们的研究结果表明，N-HiTS和NBEATSx在有限数据场景中表现具有竞争力，而基于Transformer的模型可达到相当的准确性，但往往需要更多的适配和调优。模型性能也得益于精心的特征选择和超参数调优，我们注意到最强模型之间的差异通常很小。
英文摘要
While publicly available electricity market data presents a valuable resource for forecasting research, the field lacks established benchmark datasets for standardized comparison. As a result, many studies have relied on different datasets and metrics to evaluate methods in isolated settings, making it difficult to assess progress and compare state-of-the-art approaches consistently. In this work, we use public data to evaluate deep learning models for electricity price forecasting (EPF) across multiple market settings. Our goal is to establish a reproducible framework that enables a consistent evaluation of forecasting models. Although deep learning has been explored for day-ahead EPF, many prior studies are limited to single-market settings, narrow feature sets, or fixed training regimes. This work presents a comparative evaluation of six deep learning models--covering state-space, MLP, RNN, and Transformer-based architectures--emphasizing generalization across markets. We simulate low-data target-market conditions using zero-shot, one-shot, and few-shot learning. Our test set focuses on the Germany-Luxembourg (DE-LU) bidding zone in 2024 using a standardized dataset with calendar, historical price, and market-derived features. Our findings suggest that N-HiTS and NBEATSx perform competitively in limited-data scenarios, while transformer-based models can reach comparable accuracy but tend to require more adaptation and tuning. Model performance also benefits from careful feature selection and hyperparameter tuning, and we note that the differences between the strongest models are often small.
