[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(02):59-66.[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(02):59-66.[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02期
页码:
59-66
栏目:
出版日期:
2026-02-13

文章信息/Info

Title:
Short-term Photovoltaic Power Forecasting Based on Improved Dung Beetle Optimizer for Optimizing VMD-BiLSTM
文章编号:
1671-6833(2026)02-0059-08
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 Electrical 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 TP301
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)和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的隐藏层大小和Dropout比例,提升了模型的学习能力并缓解了过拟合问题。通过山东和河北两个光伏电站的实际数据对所提模型进行实验,结果表明:相比于未改进的DBO-VMD-BiLSTM模型,所提模型在两个电站上的MAE、MAPE、RMSE均最优。
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) was proposed. Firstly, a VMD-BiLSTM-based prediction framework was constructed, where time-series data were decomposed into multiple components via VMD and fed into BiLSTM for individual prediction. The final output was 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) was 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. Compared to the unimproved model DBO-VMD-BiLSTM, the results showed that the proposed model had the best MAE, MAPE and RMSE at two power stations.

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更新日期/Last Update: 2026-03-04