We present a novel recurrent neural network architecture designed explicitly 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 stylised 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. The proposed model achieves approximately 12% higher accuracy than leading benchmarks. We evaluate the contributions of the interpretable model components and conclude on the impact of combining linear and non-linear structures.

翻译：本文提出了一种专为日前电力价格预测设计的新型循环神经网络架构，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和卡尔曼滤波器等线性结构嵌入循环网络，实现了高效计算并增强了模型的可解释性。该设计融合了线性和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进行了全面的预测研究，将本模型与最先进的方法（特别是高维线性模型和神经网络模型）进行了比较。所提出的模型比主流基准模型实现了约12%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影响。