融合自编码器与注意力机制，提升极端天气下电价预测精度。
A Hybrid Autoencoder-Transformer Model for Robust Day-Ahead Electricity Price Forecasting under Extreme Conditions
- 用自注意力机制动态加权历史数据关键段，捕捉长期趋势与短期波动。
- 自编码器无监督检测极端事件引发的异常模式，提升鲁棒性。
- 在加州与山东数据上表现优于主流方法，适合电力市场实时预测。
精准的日前电价预测（DAEPF）对电力系统高效运行至关重要，但极端条件与市场异常给现有方法带来巨大挑战。本文提出一种新型混合深度学习框架，结合精简注意力变换器（DAT）与自编码器自回归模型（ASM）。DAT利用自注意力机制动态赋予历史数据关键片段更高权重，有效捕捉长期趋势与短期波动；同时，ASM通过无监督学习检测并隔离极端条件（如暴雨、热浪或节假日）引发的异常模式。在加州与山东省份采样数据集上的实验表明，该框架在预测精度、鲁棒性与计算效率方面均显著优于当前最优方法，有望增强电网韧性并优化未来电力市场运营。
原文摘要 · Abstract (English)
Accurate day-ahead electricity price forecasting (DAEPF) is critical for the efficient operation of power systems, but extreme condition and market anomalies pose significant challenges to existing forecasting methods. To overcome these challenges, this paper proposes a novel hybrid deep learning framework that integrates a Distilled Attention Transformer (DAT) model and an Autoencoder Self-regression Model (ASM). The DAT leverages a self-attention mechanism to dynamically assign higher weights to critical segments of historical data, effectively capturing both long-term trends and short-term fluctuations. Concurrently, the ASM employs unsupervised learning to detect and isolate anomalous patterns induced by extreme conditions, such as heavy rain, heat waves, or human festivals. Experiments on datasets sampled from California and Shandong Province demonstrate that our framework significantly outperforms state-of-the-art methods in prediction accuracy, robustness, and computational efficiency. Our framework thus holds promise for enhancing grid resilience and optimizing market operations in future power systems.
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