[1]蒋建东,常轶哲,徐畅,等.基于改进蜣螂算法优化VMD-BiLSTM的短期光伏功率预测[J].郑州大学学报(工学版),2026,47(XX):1-8.[doi:10. 13705 / j. issn. 1671-6833. 2025. 05. 024]
　JIANG Jiandong,CHANG Yizhe,XU Chang,et al.Short-term Photovoltaic Power Forecasting Based on Improved Dung Beetle Optimizer for Optimizing VMD-BiLSTM[J].Journal of Zhengzhou University (Engineering Science),2026,47(XX):1-8.[doi:10. 13705 / j. issn. 1671-6833. 2025. 05. 024]
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基于改进蜣螂算法优化VMD-BiLSTM的短期光伏功率预测()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
47
期数:
2026年XX
页码:
1-8
栏目:
出版日期:
2026-09-10

文章信息/Info

Title:
Short-term Photovoltaic Power Forecasting Based on Improved Dung Beetle Optimizer for Optimizing VMD-BiLSTM
作者:
蒋建东1; 常轶哲1; 徐畅1; 郭嘉琦2; 张亦弛1
1.郑州大学 电气与信息工程学院,河南 郑州 450001;2.国网河南省电力公司 安阳供电公司,河南 安阳 455000
Author(s):
JIANG Jiandong1 ;  CHANG Yizhe1 ;  XU Chang1 ;  GUO Jiaqi2 ;  ZHANG Yichi1
1. School of E lectrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; 2. Anyang Power Supply Company, State Grid Henan Electric Power Company, Anyang 455000, China
关键词:
光伏发电; 功率预测; 改进蜣螂优化算法; 变分模态分解; 双向长短期记忆网络
Keywords:
photovoltaic power generation;  power forecasting;  improved dung beetle optimization algorithm;  variational mode decomposition;  bidirectional long short-term memory
分类号:
TM732
DOI:
10. 13705 / j. issn. 1671-6833. 2025. 05. 024
文献标志码:
A
摘要:
为了提高光伏功率短期预测精度，提出了一种融合改进蜣螂优化算法、变分模态分解（VMD）与双向长短期记忆网络（BiLSTM）的光伏功率短期预测模型。首先，构建基于VMD-BiLSTM的预测框架，通过VMD将时间序列数据分解为多个分量并输入BiLSTM进行预测，重构各分量结果以提高整体预测性能；其次，为缓解蜣螂优化算法易陷入局部最优的问题，在其运行的不同阶段引入Logistic混沌映射、Levy飞行、黄金正弦策略和自适应T分布扰动等策略进行改进，提出了改进蜣螂优化算法；最后，利用改进蜣螂优化算法分别优化VMD的分解数K与惩罚因子α、BiLSTM的隐藏层大小与Dropout比例，提升了模型的学习能力并缓解了过拟合问题。通过山东和河北两光伏电站的实际数据对所提模型进行试验，结果表明：相比于未进行改进的DBO-VMD-BiLSTM模型，所提模型在山东电站的MAE、MAPE和RMSE指标上分别降低了21.74%、27.98%和21.17%，在河北电站上分别降低了22.41%、45.95%和37.38%。
Abstract:
In order to improve the accuracy of short-term photovoltaic power forecasting, a model integrating an Improved Dung Beetle Optimizer, Variational Mode Decomposition (VMD), and Bidirectional Long Short-Term Memory (BiLSTM) is proposed. First, a VMD-BiLSTM prediction framework is constructed, where time-series data are decomposed into multiple components via VMD and fed into BiLSTM for individual prediction. The final output is obtained by reconstructing the component-level results to enhance overall prediction performance. Subsequently, to address the tendency of the Dung Beetle Optimizer (DBO) to fall into local optima, an improved DBO algorithm (IDBO) is developed through the introduction of four strategies: Logistic chaotic mapping for initialization, Levy flight for global exploration, golden sine strategy for position updating, and adaptive T-distribution perturbation for local exploitation. Finally, the IDBO was utilized to optimize critical parameters, including the decomposition number K and penalty factor α in VMD, as well as the hidden layer size and Dropout ratio in BiLSTM, thereby enhancing the model’s learning capability and mitigating overfitting. The proposed model was experimentally tested using actual data from photovoltaic power stations in Shandong and Hebei provinces. The results demonstrate that, compared to the unimproved model PBO-VMD-BiLSTM, the proposed model reduces MAE, MAPE, and RMSE by 21.74%, 27.98%, and 21.17% respectively at the Shandong site, and by 22.41%, 45.95%, and 37.38% at the Hebei site.

参考文献/References:

[1] 史如新, 王德顺, 余涛, 等. 基于 NARX 神经网络-小波分解光伏发电功率预测[J]. 郑州大学学报(工学版), 2020, 41(6): 79-84.
SHI R X, WANG D S, YU T, et al. Prediction of photovoltaic power generation based on NARX neural network-wavelet decomposition[J]. Journal of Zhengzhou University (Engineering Science), 2020, 41(6): 79-84.
[2] MA T, YANG H X, LU L. Solar photovoltaic system modeling and performance prediction[J]. Renewable and Sustainable Energy Reviews, 2014, 36: 304-315.
[3] ALMONACID F, PÉREZ-HIGUERAS P J, FERNÁNDEZ E F, et al. A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator[J]. Energy Conversion and Management, 2014, 85: 389-398.
[4] ALAM S. Prediction of direct and global solar irradiance using broadband models: Validation of REST model[J]. Renewable Energy, 2006, 31(8): 1253-1263.
[5] LI Y T, SU Y, SHU L J. An ARMAX model for forecasting the power output of a grid connected photovoltaic system[J]. Renewable Energy, 2014, 66: 78-89.
[6] PERSSON C, BACHER P, SHIGA T, et al. Multi-site solar power forecasting using gradient boosted regression trees[J]. Solar Energy, 2017, 150: 423-436.
[7] LUO X, ZHANG D X, ZHU X. Deep learning based forecasting of photovoltaic power generation by incorporating domain knowledge[J]. Energy, 2021, 225: 120240.
[8] PA M, UDDIN M N, REZAEI N. An adaptive neuro-fuzzy model-based algorithm for fault detection in PV systems[J]. IEEE Transactions on Industry Applications, 2024, 60(1): 1919-1927.
[9] 韩晓, 王涛, 韦晓广, 等. 考虑阵列间时空相关性的超短期光伏出力预测[J]. 电力系统保护与控制, 2024, 52(14): 82-94.
HAN X, WANG T, WEI X G, et al. Ultrashort-term photovoltaic output forecasting considering spatiotemporal correlation between arrays[J]. Power System Protection and Control, 2024, 52(14): 82-94.
[10] YANG C, THATTE A A, XIE L. Multitime-scale data-driven spatio-temporal forecast of photovoltaic generation[J]. IEEE Transactions on Sustainable Energy, 2015, 6(1): 104-112.
[11] 方鹏, 高亚栋, 潘国兵, 等. 基于 LSTM 神经网络的中长期光伏发电站发电量预测方法研究[J]. 可再生能源, 2022, 40(1): 48-54.
FANG P, GAO Y D, PAN G B, et al. Research on forecasting method of mid-and long-term photovoltaic generation based on LSTM neural Network[J]. Renewable Energy Resources, 2022, 40(1): 48-54.
[12] LIU W, LIU Y M, FU L, et al. Wind power forecasting method based on bidirectional long-short term memory neural network and error correction[J]. Electric Power Components and Systems, 2021, 49(13/14): 1169-1180.
[13] 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 F Z, WANG S F, ZHANG L. 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.
[14] 朱菊萍, 魏霞, 谢丽蓉, 等. 基于 VMD 和改进 BiLSTM 的短期风电功率预测[J]. 太阳能学报, 2024, 45(6): 422-428.
ZHU J P, WEI X, XIE L R, et al. Short-term wind power prediction based on vmd and improved BiLSTM[J]. Acta Energiae Solaris Sinica, 2024, 45(6): 422-428.
[15] 刘杰, 从兰美, 夏远祥, 等. 基于 DBO-VMD 和 IWOA-BiLSTM 神经网络组合模型的短期电力负荷预测[J]. 电力系统保护与控制, 2024, 52(8): 123-133.
LIU J, CONG L M, XIA Y X, et al. Short-term power load prediction based on DBO-VMD and an IWOA-BiLSTM neural network combination model[J]. Power System Protection and Control, 2024, 52(8): 123-133.
[16] 汪繁荣, 梅涛, 卢璐. 基于相似日聚类和 VMD-LTWDBO-BiLSTM 的短期光伏功率预测[J]. 智慧电力, 2024, 52(10): 56-63, 111.
WANG F R, MEI T, LU L. Short-term PV power prediction based on similar day clustering with VMD-LTWDBO-BiLSTM[J]. Smart Power, 2024, 52(10): 56-63, 111.
[17] 廖晓辉, 陈川川. 改进的 VMD-HT 在电能质量扰动检测中的应用[J]. 郑州大学学报(工学版), 2021, 42(1): 21-27.
LIAO X H, CHEN C C. Application of improved VMD-HT in power quality disturbance detection[J]. Journal of Zhengzhou University (Engineering Science), 2021, 42(1): 21-27.
[18] SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15: 1929-1958.
[19] XUE J K, SHEN B. Dung beetle optimizer: a new meta-heuristic algorithm for global optimization[J]. The Journal of Supercomputing, 2023, 79(7): 7305-7336.
[20] 彭竑, 陈江旭, 张倩, 等. 多策略改进的蜣螂搜索算法优化 3DDV-Hop 节点定位[J]. 重庆邮电大学学报(自然科学版), 2024, 36(3): 438-449.
PENG D, CHEN J X, ZHANG Q, et al. Optimization of 3DDV-Hop node localization using multi-strategy improved dung beetle search algorithm[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2024, 36(3): 438-449.

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备注/Memo

备注/Memo:
收稿日期:2025-06-02;修订日期:2025-07-06
基金项目:河南省高等学校重点科研项目(24A470009)
作者简介:蒋建东(1975— ) ,男,河南南阳人,郑州大学教授,博士,主要从事电力系统电能质量分析与控制、新能源技术等研究,E-mail:jdjiang@zzu.edu.cn。
更新日期/Last Update: 2026-01-15