基于相似日聚类和WOA-VMD-TCN-Transformer模型的短期光伏功率研究

赵丹阳; 汤旭晶; 汪恬; 郭威

doi:doi:10.19912/j.0254-0096.tynxb.2024-1187

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基于相似日聚类和WOA-VMD-TCN-Transformer模型的短期光伏功率研究

更新时间：2025-12-10

- 基于相似日聚类和WOA-VMD-TCN-Transformer模型的短期光伏功率研究
- Issue 11, Pages: 210-218(2025)
- 作者机构：
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1187
  CLC： M615
- Published Online：04 December 2025，
  
  Published：04 December 2025
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赵丹阳, 汤旭晶, 汪恬, et al. 基于相似日聚类和WOA-VMD-TCN-Transformer模型的短期光伏功率研究[J]. 2025, (11): 210-218.
DOI：

赵丹阳, 汤旭晶, 汪恬, et al. 基于相似日聚类和WOA-VMD-TCN-Transformer模型的短期光伏功率研究[J]. 2025, (11): 210-218. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1187.

摘要

针对光伏输出功率波动显著且预测难度较大的问题

提出一种基于相似日聚类的WOA-VMD-Transformer的组合光伏功率预测模型。首先

利用K-means++算法进行相似日聚类;然后

采用鲸鱼优化算法（WOA）对变分模态分解（VMD）的参数进行寻优

将光伏功率序列分解为多个本征模态函数（IMF）;将IMF分量和气象因子加权合并成新的特征向量输入后续模型;并基于TCN-Transformer模型

分别预测不同天气类型下的IMF

叠加后得到预测值。最后

以澳大利亚中部爱丽丝泉沙漠太阳能研究中心的Hanwha Solar光伏场站一年的光伏发电和气象数据作为实例

对模型的有效性加以验证。消融实验和综合评估表明

所提模型在各类天气下均可取得较高的预测精度。

Abstract

To solve the problem that PV power fluctuates significantly and is difficult to predict

this paper proposes a combined PV power prediction model based on similar day clustering and an WOA-WMD-TCN-Transformer model. Firstly

-means ++ is used to cluster similar days. Then WOA was used to optimize VMD parameters

and the PV power sequence was decomposed into multiple Intrinsic Mode functions（IMFs）. The IMF components and meteorological factors were weighted and combined into a new feature vector and fed into the subsequent model. Based on TCN-Transformer

IMF under different weather conditions can be predicted separately and the predicted value can be obtained after superposition. Finally

the photovoltaic power generation and meteorological data of Hanwha Solar Photovoltaic Station

a desert solar Research Center in Alice Springs

Central Australia

were used as an example to verify the validity of the model. Ablation experiments and comprehensive evaluation show that the proposed model can achieve high prediction accuracy under various weather conditions.

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