基于ICOA-BP的5G光伏基站短期功率预测

张展泳; 王朝民; 陈俊江; 高绪冲; 郭文豪; 覃团发

doi:10.19912/j.0254-0096.tynxb.2024-2444

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基于ICOA-BP的5G光伏基站短期功率预测

更新时间：2026-07-21

- 基于ICOA-BP的5G光伏基站短期功率预测
- 基于ICOA-BP的5G光伏基站短期功率预测
- 太阳能学报 2026年47卷第5期页码：720-731
- 作者机构：
- 作者简介：
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- DOI：10.19912/j.0254-0096.tynxb.2024-2444
  中图分类号： TM615
- 网络首发：2026-06-04，
  
  纸质出版：2026
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张展泳, 王朝民, 陈俊江, 等. 基于ICOA-BP的5G光伏基站短期功率预测[J]. 太阳能学报, 2026,47(5):720-731.

张展泳, 王朝民, 陈俊江, et al. 基于ICOA-BP的5G光伏基站短期功率预测[J]. 2026, 47(5): 720-731.
张展泳, 王朝民, 陈俊江, 等. 基于ICOA-BP的5G光伏基站短期功率预测[J]. 太阳能学报, 2026,47(5):720-731. DOI： 10.19912/j.0254-0096.tynxb.2024-2444.

张展泳, 王朝民, 陈俊江, et al. 基于ICOA-BP的5G光伏基站短期功率预测[J]. 2026, 47(5): 720-731. DOI： 10.19912/j.0254-0096.tynxb.2024-2444.

摘要

针对5G通信技术能耗高、成本大的问题

5G光伏基站（PVBSs）通过集成高性能、高效率、低成本的光伏发电技术

利用能量路由器实现了通信与能源利用的无缝结合。为确保5G光伏基站供电系统的稳定性

该文提出一种基于改进小龙虾优化算法（ICOA）优化反向传播（BP）神经网络的短期功率预测方法。ICOA通过圆混沌映射、镜像反射学习、改进天鹰座算法和莱维飞行策略

增强了算法的全局搜索能力和收敛速度

有效优化了BP神经网络的权重和偏置

提高了光伏功率预测精度。模型还采用主成分分析法捕捉数据相关性并压缩数据维度

以及循环遍历算法选择最优隐含层节点数量

确保模型性能最优。实验结果表明

改进小龙虾优化算法具有优异的寻优及收敛能力

同时

与基于北方苍鹰优化算法、灰狼优化算法、鲸鱼优化算法以及小龙虾优化算法的BP神经网络模型相比

ICOA-BP模型在不同天气条件下的预测精度均更高

对于促进5G光伏基站供电系统的稳定性具有重要意义。

Abstract

To reduce the high energy consumption and costs of 5G communication technology

5G photovoltaic base stations （PVBSs） integrate efficient and low-cost photovoltaic power generation technologies and employ energy routers to combine communication with energy utilization. To ensure the stability of the power supply system in 5G PVBSs

this study proposes a short-term power prediction method based on a back propagation （BP） neural network optimized by the improved crayfish optimization algorithm （ICOA）. The ICOA enhances global search capability and convergence speed through circular chaotic mapping

mirror reflection learning

an improved aquila optimizer

and the Lévy flight strategy. It effectively optimizes the weights and biases of the BP neural network to enhance photovoltaic power prediction accuracy. Moreover

the model employs principal component analysis to capture data correlations and reduce data dimensionality. A loop traversal algorithm is employed to select the optimal number of nodes in the hidden layer

ensuring optimal model performance. The experimental results show that the ICOA has excellent optimization and convergence capabilities. Furthermore

the ICOA-BP model achieves higher prediction accuracy under different weather conditions than BP neural network models optimized by the northern goshawk optimization algorithm

grey wolf optimization algorithm

whale optimization algorithm

and crayfish optimization algorithm. This improvement significantly enhances the stability of the power supply system in 5G PVBSs.

关键词

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references

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