摘要： ：针对当前短期电价预测方法中变分模态分解（VMD）参数优化效率低、单一预测模型特征表达能力不足及特征冗余等问题，本文提出一种基于多策略改进冠豪猪优化算法与深度学习的短期电价预测方法。首先，通过引入莱维飞行策略、周期性种群变异和动态调整参数机制改进冠豪猪优化算法（CPO），提高其全局搜索能力与收敛速度，并用于优化VMD的模态数量 和惩罚因子 参数，以提高信号分解精度。其次，构建融合特征加权的深度学习模型，通过设计动态加权模块抑制噪声干扰并强化关键特征的影响，结合sLSTM的长期依赖捕捉能力与Transformer并行计算优势，实现多尺度特征的协同优化处理。最后，构建MSICPO-VMD-WF-sLSTM-Transformer混合模型进行电价预测。实验结果表明：多策略改进冠豪猪优化算法（MSICPO）相较于原始CPO算法和其他传统优化算法，实现了VMD参数优化中的最优解精度和优化效率的精细化平衡，所提混合预测模型在预测精度方面表现良好，拟合度系数达到0.95。此外，跨区域数据预测实验也进一步验证了模型在不同区域电力市场的适用性和可泛化性。本文所提方法不仅为智能优化算法的改进与多特征预测技术提供了理论参考，而且为复杂电力市场下短期电价预测提供了高精度、强泛化的解决方案。

Abstract: Aiming at the low efficiency of parameter optimization of Variational Mode Decomposition (VMD) in current short-term electricity price prediction methods, the insufficient feature expression ability of single prediction models, and the problem of feature redundancy, this paper proposes a short-term electricity price prediction method based on Multi-Strategy Improved Crested Porcupine Optimizer (MSICPO) algorithm and deep learning. First, the Crested Porcupine Optimizer (CPO) algorithm is improved by introducing Lévy flight strategy, periodic population variation, and dynamic parameter adjustment mechanism to enhance its global search ability and convergence speed. It is used to optimize the modal number and penalty factor parameters of VMD to improve the accuracy of signal decomposition. Second, a deep learning model integrating feature weighting is constructed. By designing a dynamic weighting module to suppress noise interference and enhance the impact of key features, combined with the long-term dependency capture ability of sLSTM and the parallel computing advantage of Transformer, multi-scale feature collaborative optimization processing is realized. Finally, the MSICPO-VMD-WF-sLSTM-Transformer hybrid model is constructed for electricity price prediction. Experimental results show that the Multi-Strategy Improved Crested Porcupine Optimizer algorithm achieves a refined balance of optimal solution precision and optimization efficiency in VMD parameter optimization compared with the original CPO algorithm and other traditional optimization algorithms. The proposed hybrid forecasting model performs well in prediction accuracy, with a coefficient of determination reaching 0.95. In addition, cross-regional data prediction experiments further verify the applicability and generalization ability of the model in different regional electricity markets. The method proposed in this paper not only provides theoretical references for the improvement of intelligent optimization algorithms and multi-feature prediction technologies, but also offers a high-precision and strong generalization solution for short-term electricity price prediction in complex electricity markets.