基于CEEMDAN-KDBSCAN-Informer的日前辐照度预测方法

谢七月; 刘瑶; 申忠利; 周育才; 付强; 王晓丽

doi:10.19805/j.cnki.jcspe.2025.240454

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基于CEEMDAN-KDBSCAN-Informer的日前辐照度预测方法

新能源与储能 | 更新时间：2026-10-10

- 基于CEEMDAN-KDBSCAN-Informer的日前辐照度预测方法
- Day-ahead Irradiance Forecasting Framework Based on CEEMDAN-KDBSCAN-Informer
- 动力工程学报 2025年45卷第9期页码：1433-1441
- 作者机构：
  
  1. 长沙理工大学电气与信息工程学院,湖南,长沙,410114
  2. 中南大学自动化学院,湖南,长沙,410083
- 作者简介：
  
  谢七月(1980—),男,江西于都人,教授,博士生导师,博士,主要从事发电过程建模与控制等方面的研究,E-mail:qyxie168@163.com
- 基金信息：
  
  国家自然科学基金面上资助项目(62373067、62073342);湖南省自然科学基金面上资助项目(2023JJ30437)
- DOI：10.19805/j.cnki.jcspe.2025.240454
  中图分类号：
- 网络首发：2025-09-16，
  
  纸质出版：2025
- 稿件说明：
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谢七月,刘瑶,申忠利,周育才,付强,王晓丽. 基于CEEMDAN-KDBSCAN-Informer的日前辐照度预测方法动力工程学报, 2025, 45(9): 1433-1441 https://doi.

org/10.19805/j.cnki.jcspe.2025.240454
谢七月,刘瑶,申忠利,周育才,付强,王晓丽. 基于CEEMDAN-KDBSCAN-Informer的日前辐照度预测方法动力工程学报, 2025, 45(9): 1433-1441 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.240454.

org/10.19805/j.cnki.jcspe.2025.240454 DOI：

摘要

针对目前辐照度预测大多为小时内预测

且仅基于天气分类

没有考虑到一天之间天气的变化以及云类型对辐照度的影响等问题

提出一种基于完全自适应噪声集合经验模态分解(CEEMDAN)-K-means基于密度的聚类算法(KDBSCAN)-Informer的日前辐照度预测方法。首先对原始气象变量进行主成分分析来降低输入数据的维度

再通过CEEMDAN对主成分数据进行分解与重构

然后使用KDBSCAN方法对数据实现天气类型聚类和同一天气条件下云层的聚类

最后根据两层聚类结果

使用Informer模型进行日前直接法向辐照度(DNI)预测。结果表明:通过对云层类型进行分类

以在预测过程中考虑一天的天气变化

晴天日前辐照度预测的决定系数

达到0.98;相比于常用的辐照度预测方法

整体数据的平均绝对误差降低了32.42 W/m

均方根误差降低了31.71 W/m

达到0.97。

Abstract

Existing irradiance forecasts are mostly intra-hour models built on coarse weather-type labels

ignoring the intraday weather evolution and the impact of cloud type on irradiance. To address these limitations

a day-ahead direct normal irradiance (DNI) forecasting framework that couples complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)

K-means density-based spatial clustering of applications with noise (KDBSCAN) and the Informer model was proposed. Firstly

principal component analysis was employed to compress the dimensions of raw meteorological variables. The derived principal components were then decomposed and re

constructed via CEEMDAN. KDBSCAN was subsequently employed to perform a two-layer clustering

weather-type clustering and intra-weather-type cloud-pattern clustering. Finally

based on the two-layer labels

the day-ahead DNI was forecasted via the Informer network. Results show that by classifying cloud types to consider weather changes throughout the day in the prediction process

the determination coefficient (

) for predicting irradiance before sunny days reaches 0.98. Compared with prevailing benchmarks

the proposed framework reduces the overall mean absolute error by 32.42 W/m

and root-mean-square error by 31.71 W/m

achieving overall

of 0.97.

关键词

Keywords

references

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