ULTRA-SHORT-TERM IRRADIANCE PREDICTION BASED ON CLOUD IMAGES PHASE CORRELATION AND LOW-RANK FEATURE EXTRACTION

Guo Jiqiang; Xu Xudong; Zhang Shuai; Ren Mifeng; Wang Fang; Yan Gaowei

doi:10.19912/j.0254-0096.tynxb.2023-1799

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ULTRA-SHORT-TERM IRRADIANCE PREDICTION BASED ON CLOUD IMAGES PHASE CORRELATION AND LOW-RANK FEATURE EXTRACTION

更新时间：2025-10-18

- ULTRA-SHORT-TERM IRRADIANCE PREDICTION BASED ON CLOUD IMAGES PHASE CORRELATION AND LOW-RANK FEATURE EXTRACTION
- Vol. 46, Issue 3, Pages: 494-503(2025)
- 作者机构：
  
  1. 太原理工大学电气与动力工程学院,太原 030024
  2. 山西格盟中美清洁能源研发中心有限公司,太原 030031
- 作者简介：
- 基金信息：
- DOI：10.19912/j.0254-0096.tynxb.2023-1799
  CLC： TM615
- Published Online：07 April 2025，
  
  Published：2025
- 稿件说明：
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郭吉强, 徐旭东, 张帅, 任密蜂, 王芳, 阎高伟. 基于云图相位相关和低秩特征提取的超短期辐照度预测[J]. 太阳能学报, 2025, 46(3): 494-503.

Guo Jiqiang, Xu Xudong, Zhang Shuai, et al. ULTRA-SHORT-TERM IRRADIANCE PREDICTION BASED ON CLOUD IMAGES PHASE CORRELATION AND LOW-RANK FEATURE EXTRACTION[J]. 2025, 46(3): 494-503.
郭吉强, 徐旭东, 张帅, 任密蜂, 王芳, 阎高伟. 基于云图相位相关和低秩特征提取的超短期辐照度预测[J]. 太阳能学报, 2025, 46(3): 494-503. DOI： 10.19912/j.0254-0096.tynxb.2023-1799.

Guo Jiqiang, Xu Xudong, Zhang Shuai, et al. ULTRA-SHORT-TERM IRRADIANCE PREDICTION BASED ON CLOUD IMAGES PHASE CORRELATION AND LOW-RANK FEATURE EXTRACTION[J]. 2025, 46(3): 494-503. DOI： 10.19912/j.0254-0096.tynxb.2023-1799.

摘要

针对云的动态变化对辐照度的直接影响

基于傅里叶相位相关（PC）理论从连续地基云图获取云的动态信息矩阵（DIM）

为了减小噪声及冗余信息对模型的影响

构建低秩矩阵特征提取（LMFE）模型作为提取模块（EM）

实现DIM有效信息的提取

EM通过迭代训练得到最优特征提取矩阵W和偏置项b。同时

以双向门控循环单元（Bi-GRU）为基础搭建回归模块（RM）

RM共享EM输出W和b

从云图提取的有效信息结合历史辐照度序列作为输入

实现辐照度的多步回归预测。对真实采集的数据进行实验仿真

结果表明该预测方法可有效提高辐照度预测精度。

Abstract

Aiming at the direct impact of dynamic changes of the clouds on irradiance

this paper obtains the cloud dynamic information matrix （DIM） from continuous ground-based cloud images based on the Fourier phase correlation （PC） theory. To minimize the effects on the model led by noise and redundant information

a low-rank matrix feature extraction （LMFE） model is constructed as an extraction module （EM） to extract valid information from the DIM productively. EM obtains the optimal feature extraction matrix W and bias terms b through iteration. Simultaneously

the regression module （RM） is constructed based on the bidirectional gated recurrent unit （Bi-GRU）

where RM shares EM outputs W and b

and the valid information extracted from the cloud images is combined with the historical irradiance sequence as input to achieve multi-step regression prediction of irradiance. The experimental simulation of the actual collected data demonstrates that the prediction method can effectively improve the irradiance prediction accuracy.

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references

VERBOIS H, HUVA R, RUSYDI A, et al.Solar irradiance forecasting in the tropics using numerical weather prediction and statistical learning[J]. Solar energy, 2018, 162: 265-277.

吴凌霄, 王一楠, 旺堆, 等. 西藏羊八井地区太阳短波辐照度特征及其短期预测模型对比分析[J]. 地球物理学报, 2023, 66(8): 3144-3156.

张成, 白建波, 兰康, 等. 基于数据挖掘和遗传小波神经网络的光伏电站发电量预测[J]. 太阳能学报, 2021, 42(3): 375-382.

SI Z Y, YANG M, YU Y X, et al.Photovoltaic power forecast based on satellite images considering effects of solar position[J]. Applied energy, 2021, 302: 117514.

余光正, 陆柳, 汤波, 等. 基于云图特征提取的改进混合神经网络超短期光伏功率预测方法[J]. 中国电机工程学报, 2021, 41(20): 6989-7003.

张青山, 王丽婕, 郝颖, 等. 基于卫星云图和晴空模型的分布式光伏电站太阳辐照度超短期预测[J]. 高电压技术, 2022, 48(8): 3271-3281.

张臻, 陈天鹏, 王磊, 等. 基于地基云图的超短期太阳辐照预测方法与装置研究[J]. 太阳能学报, 2023, 44(1): 133-140.

DONG Z B, YANG D Z, REINDL T, et al.Satellite image analysis and a hybrid ESSS/ANN model to forecast solar irradiance in the tropics[J]. Energy conversion and management, 2014, 79: 66-73.

PALETTA Q, ARBOD G, LASENBY J.Omnivision forecasting: combining satellite and sky images for improved deterministic and probabilistic intra-hour solar energy predictions[J]. Applied energy, 2023, 336: 120818.

FU C L, CHENG H Y.Predicting solar irradiance with all-sky image features via regression[J]. Solar energy, 2013, 97: 537-550.

CHENG H Y, YU C C.Multi-model solar irradiance prediction based on automatic cloud classification[J]. Energy, 2015, 91: 579-587.

WANG Z J, WANG L, HUANG C, et al.A hybrid ensemble learning model for short-term solar irradiance forecasting using historical observations and sky images[J]. IEEE transactions on industry applications, 2023, 59(2): 2041-2049.

YANG H, WANG L, HUANG C, et al.3D-CNN-based sky image feature extraction for short-term global horizontal irradiance forecasting[J]. Water, 2021, 13(13): 1773.

钟振兴, 马晓波, 安巍. 基于地基云图与气象数据的辐照度轻量化预测[J]. 东北电力大学学报, 2021, 41(1): 24-30.

朱琼锋, 李家腾, 乔骥, 等. 人工智能技术在新能源功率预测的应用及展望[J]. 中国电机工程学报, 2023, 43(8): 3027-3048.

柴闵康, 夏飞, 张浩, 等. 基于云图特征自识别的光伏超短期预测模型[J]. 电网技术, 2021, 45(3): 1023-1035.

WEN H R, DU Y, CHEN X Y, et al.Deep learning based multistep solar forecasting for PV ramp-rate control using sky images[J]. IEEE transactions on industrial informatics, 2021, 17(2): 1397-1406.

BERNECKER D, RIESS C, ANGELOPOULOU E, et al.Continuous short-term irradiance forecasts using sky images[J]. Solar energy, 2014, 110: 303-315.

蒋俊霞, 高晓清, 吕清泉, 等. 基于地基云图的云跟踪与太阳辐照度超短期预报方法研究[J]. 太阳能学报, 2020, 41(5): 351-358.

刘翼飞, 崔承刚. 基于地基云图云量特征的光伏发电功率区间预测[J]. 南方电网技术, 2023, 17(2): 92-100.

王育飞, 郝德扬, 薛花, 等. 计及云图和混沌特性的光伏功率组合预测方法[J]. 太阳能学报, 2023, 44(12): 74-81.

王飞, 甄钊, 米增强, 等. 基于相位相关的天空图像云团运动速度计算方法研究[J]. 太阳能学报, 2016, 37(10): 2539-2546.

ZHEN Z, XUAN Z M, WANG F, et al.Image phase shift invariance based multi-transform-fusion method for cloud motion displacement calculation using sky images[J]. Energy conversion and management, 2019, 197: 111853.

ZHEN Z, SUN Y J, WANG F, et al.A cloud displacement estimation approach for sky images based on phase correlation theory[C]//2016 IEEE International Conference on Power System Technology (POWERCON). Wollongong, Australia, 2016: 1-6.

张雲钦, 程起泽, 蒋文杰, 等. 基于EMD-PCA-LSTM的光伏功率预测模型[J]. 太阳能学报, 2021, 42(9): 62-69.

ZHOU J H, ZHANG Q, ZENG S N, et al.Latent linear discriminant analysis for feature extraction via isometric structural learning[J]. Pattern recognition, 2024, 149: 110218.

黄冬梅, 张晓桐, 张明华, 等. 全局判别与局部稀疏保持HSI半监督特征提取[J]. 计算机工程与应用, 2019, 55(20): 184-191.

杨利锋, 林大华, 邓振云, 等. 低秩特征选择多输出回归算法[J]. 计算机工程与应用, 2017, 53(20): 116-121.

YUAN H L, LI J Y, LAI L L, et al.Low-rank matrix regression for image feature extraction and feature selection[J]. Information sciences, 2020, 522: 214-226.

李大中, 李颖宇. 基于深度学习与误差修正的超短期风电功率预测[J]. 太阳能学报, 2021, 42(12): 200-205.

PEDRO H T C, LARSON D P, COIMBRA C F M. A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods[J]. 2019, 11(3): 036102.

PARK S, KIM Y, FERRIER N J, et al.Prediction of solar irradiance and photovoltaic solar energy product based on cloud coverage estimation using machine learning methods[J]. Atmosphere, 2021, 12(3): 395.

WEN H R, DU Y, LIM E G, et al.A solar forecasting framework based on federated learning and distributed computing[J]. Building and environment, 2022, 225: 109556.

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