融合动力学结构与KMCrossformer深度学习的电价跳跃扩散预测模型

李霁珂; 胡志坚

doi:10.14188/j.1671-8844.2025.0267

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融合动力学结构与KMCrossformer深度学习的电价跳跃扩散预测模型

更新时间：2026-10-16

- 融合动力学结构与KMCrossformer深度学习的电价跳跃扩散预测模型
- Electricity price Jump-Diffusion forecasting model integrating dynamical structure and KMCrossformer deep learning
- 武汉大学学报（工学版） 2026年页码：1-13
- 作者机构：
  
  武汉大学电气与自动化学院，湖北武汉 430072
- 作者简介：
  
  李霁珂（2001-），女，硕士研究生，主要从事电力系统稳定与控制研究，E-mail: jacqueline2025@whu.edu.cn。
  胡志坚（1969-），男，教授，博士生导师，主要从事配电网优化配置与经济运行等研究，E-mail: zhijian_hu@163.com。
- 基金信息：
  
  广东电网公司重点科技项目(030600KC23100019)
- DOI：10.14188/j.1671-8844.2025.0267
  中图分类号： F407.61
- 收稿：2025-06-27，
  
  网络首发：2026-10-16，
- 稿件说明：
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李霁珂, 胡志坚. 融合动力学结构与KMCrossformer深度学习的电价跳跃扩散预测模型[J/OL]. 武汉大学学报（工学版）, 2026,1-13.

LI Jike, HU Zhijian. Electricity price Jump-Diffusion forecasting model integrating dynamical structure and KMCrossformer deep learning[J/OL]. Engineering Journal of Wuhan University, 2026, 1-13.
李霁珂, 胡志坚. 融合动力学结构与KMCrossformer深度学习的电价跳跃扩散预测模型[J/OL]. 武汉大学学报（工学版）, 2026,1-13. DOI： 10.14188/j.1671-8844.2025.0267.

LI Jike, HU Zhijian. Electricity price Jump-Diffusion forecasting model integrating dynamical structure and KMCrossformer deep learning[J/OL]. Engineering Journal of Wuhan University, 2026, 1-13. DOI： 10.14188/j.1671-8844.2025.0267.

摘要

随着电力市场环境的日益复杂，电价序列中呈现出愈加显著的非线性波动特征及尖峰行为。为提升电价预测的精度，提出一种跳跃扩散变体框架下的动力学预测模型。首先，基于局部离群因子（Local Outlier Factor

LOF）识别出局部离群点并通过时间缓冲机制聚合为跳跃事件簇；其次，引入Koopman算子对系统的非线性演化过程进行升维建模，从中解耦出跳跃项在状态空间中的主导动力学模式；最后，在升维层面将解耦模式作为高维特征嵌入至KMCrossformer跨维度注意力机制网络，在深层次融合跳跃项与扩散项的同时，实现对电价序列长期趋势与短时剧烈扰动的协同建模。研究通过澳大利亚昆士兰州数据集的算例分析证明了所提出预测模型在尖峰捕捉能力与整体预测分布拟合度方面的优越性。

Abstract

With the increasing complexity of the electricity market environment

electricity price series exhibit increasingly pronounced nonlinear fluctuations and spike behaviors. To enhance the accuracy of electricity price forecasting

this study proposes a dynamical prediction model under a Jump-Diffusion variant framework. First

the Local Outlier Factor (LOF) method is employed to identify local outliers

which are then aggregated into jump event clusters via a temporal buffering mechanism. Second

the Koopman operator is introduced to perform a lifted modeling of the system’s nonlinear evolution

enabling the decoupling of dominant dynamical modes associated with jump components in the state space. Finally

at the lifted level

the decoupled modes are embedded as high-dimensional features into the KMCrossformer network with a cross-dimensional attention mechanism. This design allows for deep integration of jump and diffusion components

thereby achieving coordinated modeling of both long-term trends and short-term abrupt disturbances in electricity price series. Case studies based on the Queensland

Australia dataset demonstrate the proposed model’s superiority in spike capturing ability and overall distributional fit of predictions.

关键词

Keywords

references

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