基于二次分解和BiGRU的超短期光伏发电功率预测

韩博; 李长青; 刘卫亮; 刘帅; 刘长良; 徐家豪; 王昕

doi:10.19805/j.cnki.jcspe.2025.230605

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基于二次分解和BiGRU的超短期光伏发电功率预测

新能源与储能 | 更新时间：2026-02-05

- 基于二次分解和BiGRU的超短期光伏发电功率预测
- Ultra-short-term Photovoltaic Power Forecasting Based on Secondary Decomposition and BiGRU
- 动力工程学报 2025年45卷第1期页码：62-69
- 作者机构：
  
  1. 华北电力大学控制与计算机工程学院,河北,保定,071000
  2. 保定市综合能源系统状态检测与优化调控重点实验室,河北,保定,071000
  3. 国家能源集团新能源技术研究院有限公司,北京,102209
- 作者简介：
  
  [ "刘卫亮(1983—),男,副教授,博士,主要从事综合能源仿真与优化控制方面的研究,E-mail:lwlfengzhiying@163.com" ]
- 基金信息：
  
  国家能源集团科技资助项目(GJNY-21-98);中央高校基本科研业务费资助项目(2023JG005、2023JC010)
- DOI：10.19805/j.cnki.jcspe.2025.230605
  中图分类号： TM615
- 网络首发：2025-01-15，
  
  纸质出版：2025
- 稿件说明：
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韩博,李长青,刘卫亮,刘帅,刘长良,徐家豪,王昕. 基于二次分解和BiGRU的超短期光伏发电功率预测动力工程学报, 2025, 45(1): 62-69 https://doi.

org/10.19805/j.cnki.jcspe.2025.230605
韩博,李长青,刘卫亮,刘帅,刘长良,徐家豪,王昕. 基于二次分解和BiGRU的超短期光伏发电功率预测动力工程学报, 2025, 45(1): 62-69 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.230605.

org/10.19805/j.cnki.jcspe.2025.230605 DOI：

摘要

针对超短期光伏发电功率预测

提出一种基于自适应噪声的完备经验模态分解(CEEMDAN)-变分模态分解(VMD)-双向门控循环单元(BiGRU)的混合预测模型。采用CEEMDAN对光伏发电功率信号进行分解

通过样本熵和K-means方法对分解后信号进行聚类重构;再利用VMD对复杂信号进行二次分解

以削弱信号的非平稳性;将分解后各信号分量作为BiGRU模型的输入进行训练、验证和预测

然后线性叠加各信号分量预测结果

得到最终预测结果。结果表明:混合预测模型的预测精度高于单一模型

验证了混合预测模型的有效性;通过对比典型天气情况下的预测效果及各项评价指标

验证了所提出方法的通用性。

Abstract

For the forecasting of ultra-short-term photovoltaic power

a hybrid prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)

variational mode decomposition (VMD) and bidirectional gated recurrent unit (BiGRU) was developed. The photovoltaic power generation signal was decomposed using CEEMDAN

and the decomposed signals were clustered and reconstructed using sample entropy and the K-means methods. Then

the VMD was applied for the secondary decomposition of complex signals to mitigate signal non-stationarity. The decomposed signal components were employed as inputs for training

validation and prediction in the BiGRU model. Subsequently

the predicted results from each signal component were linearly combined to obtain the final forecasting results. Results show that the hybrid model outperforms single models

confirming the effectiveness of the model. By comparing the forecasting performances under typical weather conditions and various evaluation metrics

the generality of the proposed method was validated.

关键词

Keywords

references

董存, 王铮, 白捷予, 等. 光伏发电功率超短期预测方法综述[J]. 高电压技术, 2023, 49(7): 2938-2951. DONG Cun, WANG Zheng, BAI Jieyu, et al. Review of ultra-short-term forecasting methods for photovoltaic power generation[J]. High Voltage Engineering, 2023, 49(7): 2938-2951.

刘传亮, 郭万贵, 孔心璇, 等. 西北地区宽功率光伏离网制氢技术进展与发展前景[J]. 动力工程学报, 2022, 42(8): 762-768. LIU Chuanliang, GUO Wangui, KONG Xinxuan, et al. Progress and development prospects of wide-power photovoltaic off-grid hydrogen production technology in northwest China[J]. Journal of Chinese Society of Power Engineering, 2022, 42(8): 762-768.

代佳豪, 肖刚, 祝培旺, 等. 太阳能布雷顿循环耦合蓄电池平抑光伏输出波动策略研究[J]. 动力工程学报, 2023, 43(6): 717-723. DAI Jiahao, XIAO Gang, ZHU Peiwang, et al. Study on fluctuation suppression strategy of solar Brayton cycle with storage battery on photovoltaic output power[J]. Journal of Chinese Society of Power Engineering, 2023, 43(6): 717-723.

杨国清, 张凯, 王德意, 等. 基于包络线聚类的多模融合超短期光伏功率预测算法[J]. 电力自动化设备, 2021, 41(2): 39-46. YANG Guoqing, ZHANG Kai, WANG Deyi, et al. Multi-mode fusion ultra-short-term photovoltaic power prediction algorithm based on envelope clustering[J]. Electric Power Automation Equipment, 2021, 41(2): 39-46.

黄雨薇, 彭道刚, 姚峻, 等. 基于SSA和K均值的TD-BP神经网络超短期光伏功率预测[J]. 太阳能学报, 2021, 42(4): 229-238. HUANG Yuwei, PENG Daogang, YAO Jun, et al. Ultra-short-term photovoltaic power forecast of TD-BP neural network based on SSA and K-means[J]. Acta Energiae Solaris Sinica, 2021, 42(4): 229-238.

文爽, 马逸骋, 孙志强. 基于GWO-EEMD-BP神经网络的光伏发电功率短期预测[J]. 中南大学学报(自然科学版), 2022, 53(12): 4799-4808. WEN Shuang, MA Yicheng, SUN Zhiqiang. Short-term prediction of photovoltaic power based on GWO-EEMD-BP[J]. Journal of Central South University (Science and Technology), 2022, 53(12): 4799-4808.

张金良, 刘子毅. 基于混合模型的超短期风速区间预测[J]. 电力系统保护与控制, 2022, 50(22): 49-58. ZHANG Jinliang, LIU Ziyi. Ultra short term wind speed interval prediction based on a hybrid model[J]. Power System Protection and Control, 2022, 50(22): 49-58.

王维高, 魏云冰, 滕旭东. 基于VMD-SSA-LSSVM的短期风电预测[J]. 太阳能学报, 2023, 44(3): 204-211. WANG Weigao, WEI Yunbing, TENG Xudong. Short-term wind power forecasting based on VMD-SSA-LSSVM[J]. Acta Energiae Solaris Sinica, 2023, 44(3): 204-211.

GUO Xifeng, WANG Kaize, YAO Shu, et al. RUL prediction of lithium ion battery based on CEEMDAN-CNN BiLSTM model[J]. Energy Reports, 2023, 9(Sup10): 1299-1306.

ZHOU Feite, HUANG Zhehao, ZHANG Changhong. Carbon price forecasting based on CEEMDAN and LSTM[J]. Applied Energy, 2022, 311: 118601.

张玺君, 郝俊. EEMD+BiGRU组合模型在短时交通流量预测中的应用[J]. 国防科技大学学报, 2023, 45(2): 73-80. ZHANG Xijun, HAO Jun. Application of EEMD+BiGRU combination model in short-term traffic flow prediction[J]. Journal of National University of Defense Technology, 2023, 45(2): 73-80.

杨锡运, 康宁, 杨雨薇, 等. 基于EEMD的SOA-KELM风电功率概率性短期区间预测[J]. 动力工程学报, 2019, 39(11): 926-933, 952. YANG Xiyun, KANG Ning, YANG Yuwei, et al. Probabilistic short-term interval forecast of wind power based on EEMD and SOA-KELM model[J]. Journal of Chinese Society of Power Engineering, 2019, 39(11): 926-933, 952.

王福忠, 王帅峰, 张丽. 基于VMD-LSTM与误差补偿的光伏发电超短期功率预测[J]. 太阳能学报, 2022, 43(8): 96-103. WANG Fuzhong, WANG Shuaifeng, ZHANG Li. Ultra short term power prediction of photovoltaic power generation based on VMD-LSTM and error compensation[J]. Acta Energiae Solaris Sinica, 2022, 43(8): 96-103.

滕陈源, 丁逸超, 张有兵, 等. 基于VMD-Informer-BiLSTM模型的超短期光伏功率预测[J]. 高电压技术, 2023, 49(7): 2961-2971. TENG Chenyuan, DING Yichao, ZHANG Youbing, et al. Ultra-short-term photovoltaic power prediction based on VMD-informer-BiLSTM model[J]. High Voltage Engineering, 2023, 49(7): 2961-2971.

庞博文, 丁月明, 杜善慧, 等. 基于CEEMDAN-BO-LSTNet的风电出力短期预测[J]. 电测与仪表, 2023, 60(9): 109-116, 170. PANG Bowen, DING Yueming, DU Shanhui, et al. Short-term forecasting of wind power output based on CEEMDAN-BO-LSTNet[J]. Electrical Measurement & Instrumentation, 2023, 60(9): 109-116, 170.

肖俊青, 金江涛, 李春, 等. 基于CEEMDAN样本熵与卷积神经网络的轴承故障诊断[J]. 动力工程学报, 2022, 42(5): 429-436. XIAO Junqing, JIN Jiangtao, LI Chun, et al. Bearing fault diagnosis based on CEEMDAN sample entropy and convolutional neural network[J]. Journal of Chinese Society of Power Engineering, 2022, 42(5): 429-436.

GAO Bixuan, HUANG Xiaoqiao, SHI Junsheng, et al. Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks[J]. Renewable Energy, 2020, 162: 1665-1683.

闫鹏, 张云鹏, 田婕, 等. 基于CEEMDAN-K-means算法的爆破振动信号去噪研究[J]. 爆破, 2023, 40(3): 184-190. YAN Peng, ZHANG Yunpeng, TIAN Jie, et al. Blasting vibration signal denoising based on CEEMDAN-K-means algorithm[J]. Blasting, 2023, 40(3): 184-190.

李颖智, 王维庆, 王海云. 基于CEEMDAN二次分解的风速预测[J]. 计算机仿真, 2023, 40(2): 89-93, 384.LI Yingzhi, WANG Weiqing, WANG Haiyun. Wind speed prediction based on the quadratic second decomposition of CEEMDAN[J]. Computer Simulation, 2023, 40(2): 89-93, 384.

LI Pengtao, ZHOU Kaile, LU Xinhui, et al. A hybrid deep learning model for short-term PV power forecasting[J]. Applied Energy, 2020, 259: 114216.

常雨芳, 杨子潇, 潘风, 等. 基于CEEMDAN-PE-WPD和多目标优化的超短期风电功率预测方法[J]. 电网技术, 2023, 47(12): 5015-5025. CHANG Yufang, YANG Zixiao, PAN Feng, et al. Ultra-short-term wind power prediction based on CEEMDAN-PE-WPD and multi-objective optimization[J]. Power System Technology, 2023, 47(12): 5015-5025.

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