计及云图和混沌特性的光伏功率组合预测方法

王育飞; 郝德扬; 薛花; 米阳

doi:10.19912/j.0254-0096.tynxb.2022-1236

PDF(2380 KB)

太阳能学报 ›› 2023, Vol. 44 ›› Issue (12) : 74-81. DOI: 10.19912/j.0254-0096.tynxb.2022-1236

计及云图和混沌特性的光伏功率组合预测方法

王育飞, 郝德扬, 薛花, 米阳

作者信息 +

COMBINED FORECASTING APPROACH OF PHOTOVOLTAIC POWER BASED ON CLOUD IMAGES AND CHAOTIC CHARACTERISTICS

Wang Yufei, Hao Deyang, Xue Hua, Mi Yang

Author information +

文章历史 +

摘要

为进一步提升运动云层引起的突变型辐照场景下光伏功率超短期预测准确度,提出一种计及云图和混沌特性的组合预测方法。首先,结合不同地基云图特征提取与匹配算法建立两阶段云速求解模型以精准预测云分布;其次,通过挖掘辐照度时间序列的混沌特性,提出一种“地基云图-光伏功率”映射关系动态建模方法以准确量化云分布对光伏功率的影响;最后,构建基于支持向量机云分类模型的组合预测方法并通过算例验证所提方法的有效性。

Abstract

To further improve the accuracy of ultra-short-term prediction of PV power under mutant irradiation scenarios caused by moving clouds, a combined prediction approach based on cloud images and chaotic characteristics is proposed. Firstly, by combining different ground-based cloud image feature extraction and matching algorithms, a two-stage calculation model of cloud motion velocity is established to precisely predict cloud distribution. Secondly, by taking advantage of the chaotic characteristics of irradiance time series, a dynamic modeling method of “ground-based cloud images-PV power” mapping relationship is proposed to accurately quantify the impact of cloud distribution on PV power. Finally, a cloud classification model based on support vector machines is used to develop the combined prediction method, and the effectiveness of the proposed approach is verified by simulation experiments.

导出引用

王育飞, 郝德扬, 薛花, 米阳. 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 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1236

Wang Yufei, Hao Deyang, Xue Hua, Mi Yang. COMBINED FORECASTING APPROACH OF PHOTOVOLTAIC POWER BASED ON CLOUD IMAGES AND CHAOTIC CHARACTERISTICS[J]. Acta Energiae Solaris Sinica. 2023, 44(12): 74-81 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1236

中图分类号： TM615

参考文献

[1] 张姗, 冬雷, 纪德洋, 等. 基于NWP相似性分析的超短期光伏发电功率预测[J]. 太阳能学报, 2022, 43(4): 142-147.
ZHANG S, DONG L, JI D Y, et al.Power forecasting of ultra-short-term photovoltaic station based on NWP similarity analysis[J]. Acta energiae solaris sinica, 2022, 43(4): 142-147.
[2] 王献志, 曾四鸣, 周雪青, 等. 基于XGBoost联合模型的光伏发电功率预测[J]. 太阳能学报, 2022, 43(4): 236-242.
WANG X Z, ZENG SM, ZHOU X Q, et al.Power forecast of photovoltaic generation based on XGBoost combined model[J]. Acta energiae solaris sinica, 2022, 43(4): 236-242.
[3] AJITH M, MARTÍNEZ-RAMÓN M. Deep learning based solar radiation micro forecast by fusion of infrared cloud images and radiation data[J]. Applied energy, 2021, 294: 117014.
[4] 倪超, 王聪, 朱婷婷, 等. 基于CNN-Bi-LSTM的太阳辐照度超短期预测[J]. 太阳能学报, 2022, 43(3): 197-202.
NI C, WANG C, ZHU T T, et al.Super-short-term forecast of solar irradiance based on CNN-BI-LSTm[J]. Acta energiae solaris sinica, 2022, 43(3): 197-202.
[5] 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.
[6] AICARDI D, MUSÉ P, ALONSO-SUÁREZ R. A comparison of satellite cloud motion vectors techniques to forecast intra-day hourly solar global horizontal irradiation[J]. Solar energy, 2022, 233: 46-60.
[7] 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.
YU G Z, LU L, TANG B, et al.An improved hybrid neural network ultra-short-term photovoltaic power forecasting method based on cloud image feature extraction[J]. Proceedings of the CSEE, 2021, 41(20): 6989-7003.
[8] 王清亮, 杨博, 应欣峰, 等. 非晴空条件下光伏发电短期功率预测方法[J]. 太阳能学报, 2022, 43(3): 188-196.
WANG Q L, YANG B, YING X F, et al.Short-term photovoltaic power forecasting method under non-clear sky condition[J]. Acta energiae solaris sinica, 2022, 43(3): 188-196.
[9] ZHEN Z, PANG S J, WANG F, et al.Pattern classification and PSO optimal weights based sky images cloud motion speed calculation method for solar PV power forecasting[J]. IEEE transactions on industry applications, 2019, 55(4): 3331-3342.
[10] ZHEN Z, LIU J M, ZHANG Z Y, et al.Deep learning based surface irradiance mapping model for solar PV power forecasting using sky image[J]. IEEE transactions on industry applications, 2020, 56(4): 3385-3396.
[11] WANG F, XUAN Z M, ZHEN Z, et al.A minutely solar irradiance forecasting method based on real-time sky image-irradiance mapping model[J]. Energy conversion and management, 2020, 220: 113075.
[12] CALDAS M, ALONSO-SUÁREZ R. Very short-term solar irradiance forecast using all-sky imaging and real-time irradiance measurements[J]. Renewable energy, 2019, 143: 1643-1658.
[13] WANG Y F, FU Y C, XUE H.Improved prediction method of PV output power based on optimised chaotic phase space reconstruction[J]. IET renewable power generation, 2020, 14(11): 1831-1840.
[14] BAZINE H, MABROUKI M.Chaotic dynamics applied in time prediction of photovoltaic production[J]. Renewable energy, 2019, 136: 1255-1265.
[15] HU R, HU W H, GÖKMEN N, et al. High resolution wind speed forecasting based on wavelet decomposed phase space reconstruction and self-organizing map[J]. Renewable energy, 2019, 140: 17-31.
[16] WANG| Y F, YANG Q X, XUE H, et al. Ultra-short-term PV power prediction model based on HP-OVMD and enhanced emotional neural network[J]. IET renewable power generation, 2022, 16(11): 2233-2247.
[17] 王育飞, 付玉超, 薛花. 计及太阳辐射和混沌特征提取的光伏发电功率DMCS-WNN预测法[J]. 中国电机工程学报, 2019, 39(S1): 63-71.
WANG Y F, FU Y C, XUE H.DMCS-WNN prediction method of photovoltaic power generation by considering solar radiation and chaotic feature extraction[J]. Proceedings of the CSEE, 2019, 39(S1): 63-71.
[18] 王育飞, 杨启星, 薛花. 考虑混沌特征的增强型大脑情绪神经网络光伏发电功率超短期预测模型[J]. 高电压技术, 2021, 47(4): 1165-1177.
WANG Y F, YANG Q X, XUE H.Ultra-short-term prediction model of enhanced brain emotional neural network considering chaotic characteristics for photovoltaic power generation[J]. High voltage engineering, 2021, 47(4): 1165-1177.
[19] WANG F, XUAN Z M, ZHEN Z, et al.A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework[J]. Energy conversion and management, 2020, 212: 112766.
[20] ZHU J S, FAN L, TIAN W, et al.Toward the ghosting phenomenon in a stereo-based map with a collaborative RGB-D repair[J]. IEEE transactions on intelligent transportation systems, 2020, 21(7): 2739-2749.
[21] 周满国, 黄艳国, 段锦锋. 基于GRU-RF模型的太阳辐照度短时预测[J]. 太阳能学报, 2022, 43(7): 166-173.
ZHOU M G, HUANG Y G, DUAN J F.Short term prediction of soral irradiance based on GRU-RF model[J]. Acta energiae solaris sinica, 2022, 43(7): 166-173.