We present a novel recurrent neural network architecture specifically designed for day-ahead electricity price forecasting, aimed at improving short-term decision-making and operational management in energy systems. Our combined forecasting model embeds linear structures, such as expert models and Kalman filters, into recurrent networks, enabling efficient computation and enhanced interpretability. The design leverages the strengths of both linear and non-linear model structures, allowing it to capture all relevant stylized price characteristics in power markets, including calendar and autoregressive effects, as well as influences from load, renewable energy, and related fuel and carbon markets. For empirical testing, we use hourly data from the largest European electricity market spanning 2018 to 2025 in a comprehensive forecasting study, comparing our model against state-of-the-art approaches, particularly high-dimensional linear and neural network models. In terms of RMSE, the proposed model achieves approximately 11% higher accuracy than the best-performing benchmark. We evaluate the contributions of the interpretable model components and conclude on the impact of combining linear and non-linear structures. We further evaluate the temporal robustness of the model by examining the stability of hyperparameters and the economic significance of key features. Additionally, we introduce a probabilistic extension to quantify forecast uncertainty.

翻译：本文提出了一种新颖的循环神经网络架构，专门为日前电价预测设计，旨在提升能源系统中短期决策与运营管理的效能。该组合预测模型将专家模型与卡尔曼滤波器等线性结构嵌入循环网络，实现了高效计算与更强的可解释性。该设计融合了线性与非线性模型结构的优势，使其能够捕捉电力市场中所有相关的典型价格特征，包括日历效应、自回归效应，以及负荷、可再生能源、相关燃料与碳市场的影响。在实证检验中，我们采用2018年至2025年欧洲最大电力市场的每小时数据开展全面的预测研究，将所提模型与前沿方法——特别是高维线性模型与神经网络模型——进行比较。在均方根误差指标上，所提模型比性能最佳的基准模型实现了约11%的精度提升。我们评估了可解释模型组件的贡献，并总结了线性与非线性结构结合的成效。此外，通过检验超参数的稳定性与关键特征的经济显著性，我们进一步评估了模型的时序稳健性。最后，我们引入了一种概率扩展方法以量化预测的不确定性。