Ultra Short Term Power Prediction of Offshore Wind Power Based on Support Vector Machine Optimized by Improved Dragonfly Algorithm
DOI:
https://doi.org/10.13052/dgaej2156-3306.3734Keywords:
Support vector machine, dragonfly algorithm, offshore power prediction.Abstract
In order to improve the prediction effect of ultra short term power of offshore
wind power, the prediction model based on support vector machine optimized
dragonfly algorithm is constructed. Based on summary of the prediction
methods of wind power, the support vector machine optimized by dragonfly
algorithm is established. Finally, prediction simulation analysis of offshore
wind power is carried out, results show that the proposed prediction model in
this research can effectively improve the computing prediction precision.
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References
Lin Wang, Rui Tao, Huanling Hu, Yu-Rong Zeng, Effective wind power
prediction using novel deep learning network: Stacked independently
recurrent autoencoder, Renewable Energy, 2021, 164(2):642–655.
Md Alamgir Hossain, Ripon K. Chakrabortty, Sondoss Elsawah,
Michael J. Ryan, Very short-term forecasting of wind power generation
using hybrid deep learning model, Journal of Cleaner Production, 2021,
(5):26564.
Shuang Han, Yan-hui Qiao, Jie Yan, Yong-qian Liu, Li Li, Zheng Wang,
Mid-to-long term wind and photovoltaic power generation prediction
based on copula function and long short term memory network, Applied
Energy, 2019, 239(4):181–191.
D.Y. Hong, T.Y. Ji, M.S. Li, Q.H. Wu, Ultra-short-term forecast of wind
speed and wind power based on morphological high frequency filter and
double similarity search algorithm, International Journal of Electrical
Power & Energy Systems, 2019, 104(1):868–879.
Marcelo Azevedo Costa, Ramiro Ruiz-Cárdenas, Leandro Brioschi
Mineti, Marcos Oliveira Prates, Dynamic time scan forecasting
for multi-step wind speed prediction, Renewable Energy, 2021,
(11):584–595.
Fei Li, Guorui Ren, Jay Lee, Multi-step wind speed prediction based on
turbulence intensity and hybrid deep neural networks, Energy Conver-
sion and Management, 2019, 186(4):306–322.
Bikram Kumar, Deepak Gupta, Universum based Lagrangian twin
bounded support vector machine to classify EEG signals, Computer
Methods and Programs in Biomedicine, 2021, 208(9):106244.
Ran An, Yitian Xu, Xuhua Liu, A rough margin-based multi-task ν-
twin support vector machine for pattern classification, Applied Soft
Computing, 2021, 112(11):107769.
Hao Zhang, Yuxin Shi, Xueran Yang, Ruiling Zhou, A firefly algorithm
modified support vector machine for the credit risk assessment of supply
chain finance, Research in International Business and Finance, 2021,
(12):101482.
Ricardo ManuelArias Velásquez, Support vector machine and tree mod-
els for oil and Kraft degradation in power transformers, Engineering
Failure Analysis, 2021, 127(9):105488.
Tao Sun, Renjie Wu, Yifan Cui, Yuejiu Zheng, Sequent extended
Kalman filter capacity estimation method for lithium-ion batteries based
on discrete battery aging model and support vector machine, Journal of
Energy Storage, 2021, 39(7):102594.
Hongfei Zhu, Lianhe Yang, Jianwu Fei, Longgang Zhao, Zhongzhi
Han, Recognition of carrot appearance quality based on deep feature
and support vector machine, Computers and Electronics in Agriculture,
, 186(7):106185.
Xiaoyu Liu, Nan Li, Hailin Mu, Miao Li, Xinxin Liu, Techno-energy-
economic assessment of a high capacity offshore wind-pumped-storage
hybrid power system for regional power system, Journal of Energy
Storage, 2021, 41(9):102892.
Behzad Golparvar, Petros Papadopoulos, Ahmed Aziz Ezzat, Ruo-Qian
Wang, A surrogate-model-based approach for estimating the first and
second-order moments of offshore wind power, Applied Energy, 2021,
(10):117286.
Jovana Dakic, Marc Cheah-Mane, Oriol Gomis-Bellmunt, Eduardo
Prieto-Araujo, Low frequency AC transmission systems for offshore
wind power plants: Design, optimization and comparison to high voltage
AC and high voltage DC, International Journal of Electrical Power &
Energy Systems, 2021, 133(12):107273.
Qian Liu, Yan Sun, Mengcheng Wu, Decision-making methodologies
in offshore wind power investments: A review, Journal of Cleaner
Production, 2021, 295(5):126459.
Lin Zhou, Pengxiang Huang, Shukai Chi, Ming Li, Hu Zhou, Hong-
bin Yu, Hongda Cao, Kai Chen, Structural health monitoring of off-
shore wind power structures based on genetic algorithm optimization
and uncertain analytic hierarchy process, Ocean Engineering, 2020,
(12):108201.
Julian David Hunt, Behnam Zakeri, Alexandre Giulietti de Barros,
Walter Leal Filho, Augusto Delavald Marques, Paulo Sérgio Franco
Barbosa, Paulo Smith Schneider, Marcelo Farenzena, Buoyancy Energy
Storage Technology: An energy storage solution for islands, coastal
regions, offshore wind power and hydrogen compression, Journal of
Energy Storage, 2021, 40(8):102746.
Bagesh Kumar, Ayush Sinha, Sourin Chakrabarti, O.P. Vyas, A fast
learning algorithm for One-Class Slab Support Vector Machines,
Knowledge-Based Systems, 2021, 228(9):107267.
Hongzheng Shen, Kongtao Jiang, Weiqian Sun, Yue Xu, Xiaoyi Ma,
Irrigation decision method for winter wheat growth period in a supple-
mentary irrigation area based on a support vector machine algorithm,
Computers and Electronics in Agriculture, 182(3):106032.
Jing Zheng, Junliang Fan, Fucang Zhang, Lifeng Wu, Yufeng Zou,
Qianlai Zhuang, Estimation of rainfed maize transpiration under vari-
ous mulching methods using modified Jarvis-Stewart model and hybrid
support vector machine model with whale optimization algorithm,
Agricultural Water Management, 2021, 249(4):106799
Wen-Chieh Cheng, Xue-Dong Bai, Brian B. Sheil, Ge Li, Fei Wang,
Identifying characteristics of pipejacking parameters to assess geo-
logical conditions using optimisation algorithm-based support vec-
tor machines, Tunnelling and Underground Space Technology, 2020,
(12):103592.
Dong Liu, Maoxun Li, Yi Ji, Qiang Fu, Mo Li, Muhammad Abrar Faiz,
Shoaib Ali, Tianxiao Li, Song Cui, Muhammad Imran Khan, Spatial-
temporal characteristics analysis of water resource system resilience in
irrigation areas based on a support vector machine model optimized
by the modified gray wolf algorithm, Journal of Hydrology, 2021,
(6):125758.
Beikun Zhang, Liyun Xu, Jian Zhang, Balancing and sequencing prob-
lem of mixed-model U-shaped robotic assembly line: Mathematical
model and dragonfly algorithm based approach, Applied Soft Comput-
ing, 2021, 98(1):106739.
Shilaja C., Arunprasath T., Internet of medical things-load optimiza-
tion of power flow based on hybrid enhanced grey wolf optimization
and dragonfly algorithm, Future Generation Computer Systems, 2019,
(9):319–330.
Dipayan Guha, Provas Kumar Roy, Subrate Banerjee, Optimal tuning
of 3 degree-of-freedom proportional-integral-derivative controller for
hybrid distributed power system using dragonfly algorithm, Computers
& Electrical Engineering, 2018, 72(11):137–153.
Maya Rocha-Ortega, Pilar Rodríguez, Alex Córdoba-Aguilar, Can drag-
onfly and damselfly communities be used as bioindicators of land use
intensification? Ecological Indicators, 2019, 107(12):105553.