Citation: | ZHANG J P, YU X J, CHEN D, et al. An improved RCSA-ANN model for the prediction of offshore short-term wind speed[J]. Acta Aerodynamica Sinica, 2022, 40(4): 110−116. DOI: 10.7638/kqdlxxb-2020.0172 |
[1] |
赵林, 朱乐东, 葛耀君. 上海地区台风风特性Monte-Carlo随机模拟研究[J]. 空气动力学学报, 2009, 27(1): 25-31. doi: 10.3969/j.issn.0258-1825.2009.01.005
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) doi: 10.3969/j.issn.0258-1825.2009.01.005
|
[2] |
崔嘉, 杨俊友, 杨理践, 等. 基于改进CFD与小波混合神经网络组合的风电场功率预测方法[J]. 电网技术, 2017, 41(1): 79-85.
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)
|
[3] |
孟洋洋, 卢继平, 孙华利, 等. 基于相似日和人工神经网络的风电功率短期预测[J]. 电网技术, 2010, 34(12): 163-167.
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)
|
[4] |
潘超, 秦本双, 蔡国伟, 等. 一种新型模块化风速预测方法[J]. 太阳能学报, 2019, 40(8): 2196-2204.
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)
|
[5] |
叶瑞丽, 郭志忠, 刘瑞叶, 等. 基于小波包分解和改进Elman神经网络的风电场风速和风电功率预测[J]. 电工技术学报, 2017, 32(21): 103-111.
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)
|
[6] |
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
|
[7] |
刘兴杰, 郑文书. 基于STCP-BP的风速实时预测方法研究[J]. 太阳能学报, 2015, 36(8): 1799-1805. doi: 10.3969/j.issn.0254-0096.2015.08.002
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) doi: 10.3969/j.issn.0254-0096.2015.08.002
|
[8] |
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
|
[9] |
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
|
[10] |
尹诗德. 基于模拟退火的混合布谷鸟算法求解公交调度问题[D]. 广州: 华南理工大学, 2018.
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).
|
[11] |
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
|
[12] |
张晓凤, 王秀英. 布谷鸟搜索算法综述[J]. 计算机工程与应用, 2018, 54(18): 8-16. doi: 10.3778/j.issn.1002-8331.1806-0215
ZHANG X F, WANG X Y. Survey of cuckoo search algorithm[J]. Computer Engineering and Applications, 2018, 54(18): 8-16. (in Chinese) doi: 10.3778/j.issn.1002-8331.1806-0215
|
[13] |
马灿. 布谷鸟搜索算法的改进研究[D]. 长沙: 湖南大学, 2017.
MA C. Research on improvement of cuckoo search algorithm[D]. Changsha: Hunan University, 2017(in Chinese).
|
[14] |
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
|
[15] |
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
|
[16] |
武小梅, 林翔, 谢旭泉, 等. 基于VMD-PE和优化相关向量机的短期风电功率预测[J]. 太阳能学报, 2018, 39(11): 3277-3285.
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)
|
[17] |
高策, 沈晓卫, 章彪, 等. 改进布谷鸟搜索算法优化支持向量机的MEMS陀螺温度零偏补偿[J]. 宇航学报, 2019, 40(7): 811-817.
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)
|
[18] |
赵帅旗, 肖辉, 刘忠兵, 等. 基于CSA-IP&O的局部遮阴下光伏最大功率点追踪[J]. 电力系统保护与控制, 2020, 48(5): 26-32.
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)
|
[19] |
邹倩颖, 王小芳. 粒子群优化BP神经网络在步态识别中的研究[J]. 实验技术与管理, 2019, 36(8): 130-133, 138.
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)
|
[20] |
王硕禾, 张嘉姗, 陈祖成, 等. 基于改进深度信念网络的风电场短期风速预测[J]. 可再生能源, 2020, 38(11): 1489-1494. doi: 10.3969/j.issn.1671-5292.2020.11.011
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) doi: 10.3969/j.issn.1671-5292.2020.11.011
|
[21] |
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
|
[22] |
刘帆, 解仑, 李秉杰, 等. 多感官群集智能算法及其在前向神经网络训练方面的应用[J]. 北京科技大学学报, 2008, 30(9): 1061-1066. doi: 10.3321/j.issn:1001-053X.2008.09.020
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) doi: 10.3321/j.issn:1001-053X.2008.09.020
|
[23] |
胡江强, 郭晨, 李铁山. 启发式自适应免疫克隆算法[J]. 哈尔滨工程大学学报, 2007, 28(1): 1-5. doi: 10.3969/j.issn.1006-7043.2007.01.001
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) doi: 10.3969/j.issn.1006-7043.2007.01.001
|
[24] |
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
|
[25] |
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
|
[26] |
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
|
[27] |
李新宇. 风能资源评估方法讨论与风电场选址评价[D]. 兰州: 兰州理工大学, 2013.
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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