基于CMAPPO动态融合双分支KAN增强模型的短期风电功率预测

张鑫; 付文龙; 刘远; 孔泽昊; 李楠; 陈曦

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基于CMAPPO动态融合双分支KAN增强模型的短期风电功率预测

张鑫¹, 付文龙^1,2, 刘远¹, 孔泽昊¹, 李楠¹, 陈曦¹

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Dynamic Fusion of Dual-Branch KAN-Enhanced Networks for Wind Power Forecasting via Cross-Modal Deep Reinforcement Learning

ZHANG Xin¹, FU Wenlong^1,2, LIU Yuan¹, KONG Zehao¹, LI Nan¹, CHEN Xi¹

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摘要

【目的】受气象变化及风电功率非平稳性的影响,风电功率序列具有较强随机波动特征,传统预测模型难以实现有效建模。为提高短期风电功率预测精度,本文提出一种基于双分支KAN增强模型与跨模态深度强化学习的预测方法。【方法】首先,采用皮尔逊相关系数筛选气象特征;其次,利用改进海狸行为优化算法（improved beaver behavior optimization,IBBO）优化变分模态分解参数,实现功率序列多尺度分解。进一步构建双分支预测模型,其中KAN增强Transformer模型用于提取全局时序特征,TCN-KAN模型用于挖掘局部动态特征。在此基础上,引入跨模态注意力驱动的近端策略优化（proximal policy optimization,PPO）动态融合模块,实现多模型预测结果的自适应融合,最终获得风电功率预测结果。【结果】基于某99MW风电场2020年全年运行数据开展实验,并与多种基础预测模型进行对比。结果表明,所提方法在全年独立测试数据中具有较好的预测精度。【结论】本文方法能够有效提升短期风电功率预测性能,在全年运行数据范围内表现出较好的稳定性。

Abstract

[Objective] Affected by meteorological variations and the non-stationary characteristics of wind power generation,wind power sequences exhibit strong stochastic fluctuations,making it difficult for traditional prediction models to achieve effective modeling. To improve the accuracy of short-term wind power forecasting,this paper proposes a forecasting method based on a dual-branch KAN-enhanced model and cross-modal deep reinforcement learning. [Methods] First,meteorological features are selected using the Pearson correlation coefficient. Subsequently,an improved beaver behavior optimization (IBBO) algorithm is employed to optimize the parameters of variational mode decomposition (VMD),thereby achieving multi-scale decomposition of wind power sequences. Furthermore,a dual-branch prediction framework is constructed,where the KAN-enhanced Transformer model is utilized to extract global temporal dependencies,while the TCN-KAN model is designed to capture local dynamic patterns. On this basis,a cross-modal attention-driven proximal policy optimization (PPO)-based dynamic fusion module is introduced to adaptively integrate the prediction results from multiple models,ultimately obtaining accurate wind power forecasting results. [Results] Experiments are conducted using the operational data collected from a 99 MW wind farm throughout the year 2020,and comparisons are made with several baseline forecasting models. The results demonstrate that the proposed method achieves superior forecasting accuracy on the independent test dataset covering the entire year. [Conclusions] The proposed method effectively improves short-term wind power forecasting performance and exhibits good predictive stability under year-round operational conditions.

导出引用

张鑫, 付文龙, 刘远, 孔泽昊, 李楠, 陈曦. 基于CMAPPO动态融合双分支KAN增强模型的短期风电功率预测[J]. 电力建设. 0

ZHANG Xin, FU Wenlong, LIU Yuan, KONG Zehao, LI Nan, CHEN Xi. Dynamic Fusion of Dual-Branch KAN-Enhanced Networks for Wind Power Forecasting via Cross-Modal Deep Reinforcement Learning[J]. Electric Power Construction. 0

中图分类号： TM614

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