随机算法改进的RCSA-ANN模型及近海短期风速预测

张建平; 于新建; 陈栋; 纪海鹏

doi:10.7638/kqdlxxb-2020.0172

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随机算法改进的RCSA-ANN模型及近海短期风速预测

研究论文 | 更新时间：2026-05-28

- 随机算法改进的RCSA-ANN模型及近海短期风速预测
- An improved RCSA-ANN model for the prediction of offshore short-term wind speed
- 空气动力学学报 2022年40卷第4期页码：110-116
- 作者机构：
  
  1. 上海理工大学机械工程学院,上海,200093
  2. 上海电力大学能源与机械工程学院,上海,200090
- 作者简介：
  
  张建平*（1972-），男，江苏南京人，博士后，教授，主要研究方向：风资源评估，海上风力发电技术，PM2.5电磁除尘技术，节能发光器件寿命预测，等. E-mail：jpzhanglzu@163.com
- 基金信息：
  
  国家自然科学基金（12172228，11572187）;上海市自然科学基金（22ZR1444400）
- DOI：10.7638/kqdlxxb-2020.0172
  中图分类号： P412.16
- 纸质出版：2022
- 稿件说明：
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张建平, 于新建, 陈栋, 等. 随机算法改进的RCSA-ANN模型及近海短期风速预测[J]. 空气动力学学报, 2022, 40(4): 110−116.

ZHANG Jianping, YU Xinjian, CHEN Dong, et al. An improved RCSA-ANN model for the prediction of offshore short-term wind speed[J]. 2022, 40(4): 110-116.
张建平, 于新建, 陈栋, 等. 随机算法改进的RCSA-ANN模型及近海短期风速预测[J]. 空气动力学学报, 2022, 40(4): 110−116. DOI： 10.7638/kqdlxxb-2020.0172

ZHANG Jianping, YU Xinjian, CHEN Dong, et al. An improved RCSA-ANN model for the prediction of offshore short-term wind speed[J]. 2022, 40(4): 110-116. DOI： 10.7638/kqdlxxb-2020.0172.

摘要

为了提高近海短期风速的预测精度，提出了一种基于随机布谷鸟搜索算法（Random Cuckoo Search Algorithm，RCSA）和人工神经网络（Artificial Neural Network，ANN）的模型。首先通过引入随机因子改进布谷鸟搜索算法得到了RCSA，建立了预测海上短期风速的RCSA-ANN模型；其次在上海芦潮港建立了测风塔，测得了近海气象数据，并开展了模型的训练；最后与BP-ANN、CSA-ANN模型进行对比和分析，验证了RCSA-ANN模型的精度。结果表明：CSA改进方法简单、可靠且有效，解决了该算法易陷入局部最优的问题；RCSA-ANN模型的平均误差不仅低于BP-ANN模型的，而且远低于CSA-ANN模型的，三种模型的预测精度依次降低；RCSA-ANN模型预测精度高，能对较为波动的风速序列实现准确预测，具有很好的应用潜力。

Abstract

In order to improve the prediction accuracy of offshore short-term wind speed

a model based on the random cuckoo search algorithm (RCSA) and artificial neural network (ANN) was proposed. Firstly

RCSA was obtained by introducing a random factor into CSA

and then a RCSA-ANN model for predicting offshore short-term wind speed was established. Secondly

the training of the model was carried out by using the offshore meteorological data measured at the wind tower in Luchao Port

Shanghai. Finally

the precision of RCSA-ANN model was verified by compariative analyses. Results show that the improved CSA method is simple

reliable

and effective. And it is not easy to fall into local optimum like other models. Moreover

the average error of the RCSA-ANN model is lower than those of the BP-ANN and CSA-ANN models. Since the RCSA-ANN model can predict fluctuating wind speed sequences with high prediction accuracy

it has a promising potential in the meteorological field.

关键词

Keywords

references

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