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基于多尺度特征融合与超参数优化的双向门控循环单元电价预测

Electricity Price Forecasting Based on Multi-Scale Feature Fusion and Hyperparameter-Optimized Bidirectional GRU

  • 摘要: 针对电力市场电价的强波动性、非线性与时变性特征,文章提出了一种融合多尺度特征提取与自动化超参数优化的双向门控循环单元模型(H-O-GRU)。该模型通过特征重要性门控、多尺度时间池化与多头自注意力机制实现多时间尺度下的电价动态建模,并利用基于树结构Parzen估计(TPE)的优化算法自适应调整关键超参数。

     

    Abstract: To address the strong volatility, nonlinearity, and time-varying nature of electricity prices in the electricity market, this paper proposes a Bidirectional Gated Recurrent Unit model integrating Multi-Scale Feature Fusion and Automated Hyperparameter Optimization (H-O-GRU). The proposed framework incorporates feature-importance gating, multi-scale temporal pooling, and a multi-head self-attention mechanism to model electricity price dynamics across multiple temporal scales.

     

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