Photovoltaic Ultra-short-term Power Prediction Based on Dual-view Point Cloud Cluster Position and Motion Information

ZOU Lianglin; QUAN Hongyang; XU Xiaoshi; TANG Ping; NIU Yisen; SONG Jifeng

doi:10.19805/j.cnki.jcspe.2026.250295

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Photovoltaic Ultra-short-term Power Prediction Based on Dual-view Point Cloud Cluster Position and Motion Information

更新时间：2026-10-10

- Photovoltaic Ultra-short-term Power Prediction Based on Dual-view Point Cloud Cluster Position and Motion Information
- Chinese Journal of Power Engineering Vol. 46, Issue 5, Pages: 107-116,136(2026)
- 作者机构：
  
  1. 华北电力大学新能源学院,北京,102206
  2. 华北电力大学能源电力创新研究院,北京,102206
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2026.250295
  CLC： TM615
- Published：2026
- 稿件说明：
移动端阅览
邹良林, 全红阳, 徐孝师, 唐平, 牛一森, 宋记锋. 基于双视点云团位置和运动信息的光伏超短期功率预测[J]. 动力工程学报, 2026, 46(5): 107-116,136.
邹良林, 全红阳, 徐孝师, 唐平, 牛一森, 宋记锋. 基于双视点云团位置和运动信息的光伏超短期功率预测[J]. 动力工程学报, 2026, 46(5): 107-116,136. DOI： 10.19805/j.cnki.jcspe.2026.250295.

DOI： 10.19805/j.cnki.jcspe.2026.250295

摘要

光伏电站的功率波动性对电网安全稳定运行提出了挑战。尤其在多云天气下

云团的位置和运动情况会导致光伏出力的剧烈波动。为提高超短期功率预测的精度

提出了一种融合双视点云团位置和运动信息的混合预测框架。首先

基于地基云图的分割技术、云运动评估技术和双视点几何反演技术提取云团的高度、速度、方向及云量特征;其次

结合气象与历史功率

通过主成分分析降维和融合多源特征;最后

采用长短期记忆(LSTM)、Informer、门控循环单元(GRU)和卷积神经网络(CNN)共4种子模型并行预测

并设计投票集成策略优化输出。实验结果表明:融入云团信息使预测归一化均方根误差(RNRMSE)降低至0.117 9

较传统方法提升4.3%;异构模型融合进一步优化性能

其精度优于单一模型

提升率为0.8%~5.6%。

Abstract

Power fluctuations of photovoltaic (PV) plants present challenges to the safe and stable operation of power grids. Particularly under cloudy conditions

the position and movement of cloud clusters can cause drastic fluctuations in PV power output. To improve the accuracy of ultra-short-term power prediction

a hybrid prediction framework integrating dual-view point cloud cluster position and motion information was proposed. First

cloud cluster features— including height

velocity

direction and cloud cover— were extracted using ground-based cloud image segmentation

cloud motion estimation

and dual-view point geometric inversion technologies. Next

meteorological and historical power data were incorporated

with principal component analysis applied for dimensionality reduction and multi-source feature fusion. Finally

four sub-models—long and short term memory (LSTM)

Informer

gated recurrent unit (GRU)

and convolutional neural network (CNN)—were employed for parallel prediction

and a voting ensemble strategy was designed to optimize the output. Experimental results demonstrate that incorporating cloud cluster information reduces the prediction normalized root mean square error (RNRMSE) to 0.117 9

an improvement of 4.3% over traditional methods. Heterogeneous model fusion further enhances performance

with accuracy surpassing that of any single model by 0.8%-5.6%.

关键词

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references

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