一种高分辨率的光伏短期功率预测模型

张飞; 廖乾国; 李兴财; 胡伟伟; 薄天利; 马鑫

doi:doi:10.19912/j.0254-0096.tynxb.2024-1190

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一种高分辨率的光伏短期功率预测模型

更新时间：2025-12-10

- 一种高分辨率的光伏短期功率预测模型
- Issue 11, Pages: 235-243(2025)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1190
  CLC： M615
- Published Online：04 December 2025，
  
  Published：04 December 2025
- 稿件说明：
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张飞, 廖乾国, 李兴财, et al. 一种高分辨率的光伏短期功率预测模型[J]. 2025, (11): 235-243.
DOI：

张飞, 廖乾国, 李兴财, et al. 一种高分辨率的光伏短期功率预测模型[J]. 2025, (11): 235-243. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1190.

摘要

提出一种新颖的混合预测模型

该模型采用麻雀搜索算法（SSA）优化变分模态分解（VMD）的参数。随后

模型利用VMD和带有自适应噪声的完整集成经验模态分解（CEEMDAN）对光伏功率数据进行二次分解。接着

应用卷积神经网络和带有注意力机制的长短期记忆网络（CNN-LSTM-Attention）对分解后的序列数据进行训练和预测。对所提模型在不同天气条件和分时间辨率下进行验证

结果表明在不同时间分辨率（2或10 min）、预测范围（1、3或5 d）和不同天气条件下

该方法的预测性能均优于其他方法

预测值的相关系数均超过0.97。

Abstract

This study introduces a novel hybrid forecasting model

which uniquely employs the sparrow search algorithm to optimize the parameters of variational model Decomposition. Subsequently

the model utilizes variational mode decomposition and complete ensemble empirical mode decomposition with adaptive noise for dual rounds of time series decomposition related to solar power

followed by the application of convolutional neural networks and long short-term memory with attention mechanisms to train and forecast the decomposed series data. The proposed model undergoes validation using real-world data across various weather conditions and resolutions. The results consistently demonstrate the superior predictive performance of the new method across different time resolutions （2 or 10 minutes）

forecast ranges （1

or 5 days）

and diverse weather conditions

with a remarkable correlation coefficient of the predicted values exceeding 0.97.

关键词

Keywords

references

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