An improved RCSA-ANN model for the prediction of offshore short-term wind speed

ZHANG Jianping; YU Xinjian; CHEN Dong; JI Haipeng

doi:10.7638/kqdlxxb-2020.0172

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An improved RCSA-ANN model for the prediction of offshore short-term wind speed

更新时间：2026-05-28

- An improved RCSA-ANN model for the prediction of offshore short-term wind speed
- Vol. 40, Issue 4, Pages: 110-116(2022)
- 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：
- 基金信息：
- DOI：10.7638/kqdlxxb-2020.0172
  CLC： P412.16
- Published：2022
- 稿件说明：
移动端阅览
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

ZHAO L, ZHU L D, GE Y J. Monte-Carlo simulation about typhoon extreme value wind characteristics in Shanghai region[J]. Acta Aerodynamica Sinica, 2009, 27(1): 25-31. (in Chinese)

CUI J, YANG J Y, YANG L J, et al. New method of combined wind power forecasting based on improved CFD and wavelet-HNN model[J]. Power System Technology, 2017, 41(1): 79-85. (in Chinese)

MENG Y Y, LU J P, SUN H L, et al. Short-term wind power forecasting based on similar days and artificial neural network[J]. Power System Technology, 2010, 34(12): 163-167. (in Chinese)

PAN C, QIN B S, CAI G W, et al. A new modular forecasting method of wind speed[J]. Acta Energiae Solaris Sinica, 2019, 40(8): 2196-2204. (in Chinese)

YE R L, GUO Z Z, LIU R Y, et al. Wind speed and wind power forecasting method based on wavelet packet decomposition and improved Elman neural network[J]. Transactions of China Electrotechnical Society, 2017, 32(21): 103-111. (in Chinese)

GUPTA D, RODRIGUES J J P C, SUNDARAM S, et al. Usability feature extraction using modified crow search algorithm: a novel approach[J]. Neural Computing and Applications, 2020, 32(15): 10915-10925. DOI:10.1007/s00521-018-3688-6

LIU X J, ZHENG W S. Study on real-time forecasting method of wind speed based on stcp-bp[J]. Acta Energiae Solaris Sinica, 2015, 36(8): 1799-1805. (in Chinese)

CHEN J, ZENG G Q, ZHOU W N, et al. Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization[J]. Energy Conversion and Management, 2018, 165: 681-695. DOI:10.1016/j.enconman.2018.03.098

YANG X S. Cuckoo search and firefly algorithm: overview and analysis [M]//YANG X S, eds. Cuckoo Search and Firefly Algorithm. Studies in Computational Intelligence, vol 516. Springer, Cham, 2013.doi:10.1007/978-3-319-02141-6_1

YIN S D. Hybrid cuckoo algorithm based on simulated annealing for solving bus scheduling problem[D]. Guangzhou: South China University of Technology, 2018(in Chinese).

CHEN X J, JIN S Q, QIN S S, et al. Short-term wind speed forecasting study and its application using a hybrid model optimized by cuckoo search[J]. Mathematical Problems in Engineering, 2015, Article ID 608597. DOI:10.1155/2015/608597

ZHANG X F, WANG X Y. Survey of cuckoo search algorithm[J]. Computer Engineering and Applications, 2018, 54(18): 8-16. (in Chinese)

MA C. Research on improvement of cuckoo search algorithm[D]. Changsha: Hunan University, 2017(in Chinese).

MAKHDOOMI S, ASKARZADEH A. Optimizing operation of a photovoltaic/diesel generator hybrid energy system with pumped hydro storage by a modified crow search algorithm[J]. Journal of Energy Storage, 2020, 27: 101040. DOI:10.1016/j.est.2019.101040

İNCI M, CALISKAN A. Performance enhancement of energy extraction capability for fuel cell implementations with improved Cuckoo search algorithm[J]. International Journal of Hydrogen Energy, 2020, 45(19): 11309-11320. DOI:10.1016/j.ijhydene.2020.02.069

WU X M, LIN X, XIE X Q, et al. Short-term wind power forecasting based on variational mode decomposition-permutation entrop yand optimized relevance vector machine[J]. Acta Energiae Solaris Sinica, 2018, 39(11): 3277-3285. (in Chinese)

GAO C, SHEN X W, ZHANG B, et al. Temperature compensation of MEMS-gyro based on improving cuckoo search and support vector machines[J]. Journal of Astronautics, 2019, 40(7): 811-817. (in Chinese)

ZHAO S Q, XIAO H, LIU Z B, et al. Photovoltaic maximum power point tracking under partial shading based on CSA-IP O[J]. Power System Protection and Control, 2020, 48(5): 26-32. (in Chinese)

ZOU Q Y, WANG X F. Application of BP neural network based on particle swarm optimization in gait recognition[J]. Experimental Technology and Management, 2019, 36(8): 130-133, 138. (in Chinese)

WANG S H, ZHANG J S, CHEN Z C, et al. Short-term wind speed forecasting of wind farm based on improved deep belief network[J]. Renewable Energy Resources, 2020, 38(11): 1489-1494. (in Chinese)

ZHANG L N, LIU L Q, YANG X S, et al. A novel hybrid firefly algorithm for global optimization[J]. PLoS One, 2016, 11(9): e0163230. DOI:10.1371/journal.pone.0163230

LIU F, XIE L, LI B J, et al. Multi-sense swarm intelligence algorithm and its application in feed-forward neural networks training[J]. Journal of University of Science and Technology Beijing, 2008, 30(9): 1061-1066. (in Chinese)

HU J Q, GUO C, LI T S. Heuristic adaptive immune clone algorithm[J]. Journal of Harbin Engineering University, 2007, 28(1): 1-5. (in Chinese)

AL-MUHAMMED M J, ABU ZITAR R. Probability-directed random search algorithm for unconstrained optimization problem[J]. Applied Soft Computing, 2018, 71: 165-182. DOI:10.1016/j.asoc.2018.06.043

NUÑEZ L, REGIS R G, VARELA K. Accelerated Random Search for constrained global optimization assisted by Radial Basis Function surrogates[J]. Journal of Computational and Applied Mathematics, 2018, 340: 276-295. DOI:10.1016/j.cam.2018.02.017

NOURANI ESFETANG N, KAZEMZADEH R. A novel hybrid technique for prediction of electric power generation in wind farms based on WIPSO, neural network and wavelet transform[J]. Energy, 2018, 149: 662-674. DOI:10.1016/j.energy.2018.02.076

LI X Y. Discussion the method of wind assessment and site selection for the wind farm[D]. Lanzhou: Lanzhou University of Technology, 2013(in Chinese).

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