基于ICEEMDAN-KPCA-ICPA-LSTM的光伏发电功率预测

姚钦才; 向文国; 陈时熠; 曹敬; 郑涛

doi:10.19805/j.cnki.jcspe.2025.230777

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基于ICEEMDAN-KPCA-ICPA-LSTM的光伏发电功率预测

新能源与储能 | 更新时间：2026-02-02

- 基于ICEEMDAN-KPCA-ICPA-LSTM的光伏发电功率预测
- Photovoltaic Power Forecasting Based on ICEEMDAN-KPCA-ICPA-LSTM
- 动力工程学报 2025年45卷第3期页码：374-382
- 作者机构：
  
  1. 东南大学能源与环境学院,江苏,南京,210096
  2. 国电南瑞科技股份有限公司,江苏,南京,211006
- 作者简介：
  
  [ "姚钦才(1998—),男,陕西西安人,硕士研究生,研究方向为光伏发电功率预测" ]
  [ "陈时熠(通信作者),男,副教授,博士,E-mail:sychen@seu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金资助项目(52376101);江苏省科技厅创新能力建设专项资助项目(BM2019001);江苏省科协青年科技人才托举工程资助项目(2021-056)
- DOI：10.19805/j.cnki.jcspe.2025.230777
  中图分类号： TM615;TP18
- 网络出版：2025-03-15，
  
  纸质出版：2025
- 稿件说明：
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姚钦才,向文国,陈时熠,曹敬,郑涛. 基于ICEEMDAN-KPCA-ICPA-LSTM的光伏发电功率预测动力工程学报, 2025, 45(3): 374-382 https://doi.

org/10.19805/j.cnki.jcspe.2025.230777
姚钦才,向文国,陈时熠,曹敬,郑涛. 基于ICEEMDAN-KPCA-ICPA-LSTM的光伏发电功率预测动力工程学报, 2025, 45(3): 374-382 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.230777.

org/10.19805/j.cnki.jcspe.2025.230777 DOI：

摘要

光伏发电预测对于新型电力系统的平稳运行至关重要。针对光伏发电短期预测

提出了一种融合改进的完全自适应噪声集合经验模态分解(ICEEMDAN)、核主成分分析(KPCA)和改进的食肉植物算法(ICPA)与长短期记忆网络(LSTM)的光伏发电预测方法。首先

该方法通过ICEEMDAN提取气象数据中非线性信号的隐含特征;其次

采用核主成分分析降低分解后产生的冗余信息

并根据主成分贡献率大小选取模型输入参数;最后

对食肉植物算法(CPA)进行改进

构建ICPA-LSTM模型

并开展了晴天、雨天、多云和多变天气4种典型天气类型下光伏发电功率预测校验。结果表明:在不同天气情况下

所提模型的决定系数

均大于99%

相较于对照模型具有更好的预测性能。

Abstract

Accurate forecasting of photovoltaic (PV) power is essential for stable operation of new electricity systems. This study proposed a novel approach for short-term PV power forecasting combining the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN)

kernel principal component analysis (KPCA) and an improved carnivorous plant algorithm-long short-term memory (ICPA-LSTM) network. First

ICEEMDAN was employed to extract the implicit features of nonlinear signals from the meteorological data. Next

KPCA was performed to reduce the redundancy of the decomposed data and select model input parameters based on the contribution of princip

al components. Finally

the ICPA-LSTM model was constructed by improving the carnivorous plant algorithm (CPA). The approach was validated for PV power prediction under four typical weather conditions: sunny

rainy

cloudy

and variable weather. Results show that the proposed model reaches a determination coefficient (

) of over 99% across all four weather scenarios

and achieves better performance compared to benchmark models.

关键词

Keywords

references

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