基于气象分类的超短时光伏发电预测模型

顾锦; 刘宇; 顾雷涛; 焦尚; 周瑾白; 徐正蓺

doi:doi:10.19912/j.0254-0096.tynxb.2024-1385

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基于气象分类的超短时光伏发电预测模型

更新时间：2026-01-08

- 基于气象分类的超短时光伏发电预测模型
- Vol. 46, Issue 12, Pages: 263-270(2025)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1385
  CLC： TM615
- Published Online：07 January 2026，
  
  Published：2025
- 稿件说明：
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顾锦, 刘宇, 顾雷涛, et al. 基于气象分类的超短时光伏发电预测模型[J]. 2025, 46(12): 263-270.
DOI：

顾锦, 刘宇, 顾雷涛, et al. 基于气象分类的超短时光伏发电预测模型[J]. 2025, 46(12): 263-270. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1385.

摘要

针对传统基于相似日聚类方法无法实现短时变化气象类型的低成本准确识别

从而影响光伏功率预测精度的问题

该文融合理想GHI与实测GHI的差分数据特征

利用XGBoost算法进行超短时气象场景分类计算

然后采用VMD-CNN-LSTM组合预测模型

捕捉时序数据局部和全局的特征

提高预测准确性。实验结果表明

该方法在实际应用时无需额外高成本的气象观测方式

即可实现超短时光伏发电更高精度的预测。相比传统预测算法

晴天理想天气下

30 min预测的指标R2上升3.4%

指标MSE、MAE和RMSE分别下降52.6%、33.0%和29.0%。

Abstract

To address the limitations of traditional similar-day clustering methods

which fail to achieve low-cost and accurate identification of short-term variations in weather types and thus affect photovoltaic （PV） power forecasting accuracy

this study integrates the differential features between ideal global horizontal irradiance （GHI） and measured GHI

employing the XGBoost algorithm for ultra-short-term weather scenario classification. Subsequently

a combined VMD-CNN-LSTM prediction model is adopted to capture both local and global characteristics of time-series data

thereby improving forecasting accuracy. Experimental results demonstrate that the proposed method can achieve higher accuracy in ultra-short-term PV power forecasting without requiring additional high-cost meteorological observations. Compared with traditional forecasting algorithms

under ideal sunny conditions

the 30-minute-ahead forecasting performance achieves an ² increase of 3.4%

while the MSE

MAE

and RMSE are reduced by 52.6%

33%

and 29%

respectively.

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Keywords

references

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