A combination forecasting model of wind speed based on decomposition

Due to the intermittent, fluctuating and random characteristics of wind system, the output of wind power will become unstable with the change of wind, which brings severe challenges to the safe and stable operation of the power system. An effective way to solve this problem is to accurately forecast...

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Published in:Energy reports Vol. 7; pp. 1217 - 1233
Main Authors: Tian, Zhongda, Li, Hao, Li, Feihong
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.11.2021
Elsevier
Subjects:
Combination forecasting
Echo state network
Improved whale optimization algorithm
Variational mode decomposition
Wind speed
Improved whale optimization algorithm
Combination forecasting
Variational mode decomposition
Wind speed
Echo state network
ISSN:2352-4847, 2352-4847
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Abstract Due to the intermittent, fluctuating and random characteristics of wind system, the output of wind power will become unstable with the change of wind, which brings severe challenges to the safe and stable operation of the power system. An effective way to solve this problem is to accurately forecast the wind speed. This paper presents a novel wind speed combination forecasting model based on decomposition. The innovation of the forecasting model is as follows. (a) In view of the unstable characteristics of wind speed, variational mode decomposition algorithm is introduced to decompose the historical wind speed data to obtain a series of stable components with different frequencies. (b) Echo state network with good forecasting ability is selected as the forecasting model of each component. (c) To solve the problem that the forecasting performance of echo state network is greatly affected by the parameters of the reservoir, an improved whale optimization algorithm is proposed to optimize these parameters. The optimized echo state network improves the forecasting effect. (d) The final forecasting results are obtained by adding the forecasting values of each component. (e) The performance of the developed forecasting model is verified by using two actual collected data sets of ultra-short-term wind speed and short-term wind speed. Compared with some state-of-the-art forecasting models, the comparison result curve between the forecasting value and actual value of wind speed, the forecasting error distribution, the histogram of the forecasting error distribution, the performance indicators, related statistical indicators, and Taylor diagram show that the developed forecasting model has higher prediction accuracy and is able to reflect the change laws of wind speed correctly.
AbstractList Due to the intermittent, fluctuating and random characteristics of wind system, the output of wind power will become unstable with the change of wind, which brings severe challenges to the safe and stable operation of the power system. An effective way to solve this problem is to accurately forecast the wind speed. This paper presents a novel wind speed combination forecasting model based on decomposition. The innovation of the forecasting model is as follows. (a) In view of the unstable characteristics of wind speed, variational mode decomposition algorithm is introduced to decompose the historical wind speed data to obtain a series of stable components with different frequencies. (b) Echo state network with good forecasting ability is selected as the forecasting model of each component. (c) To solve the problem that the forecasting performance of echo state network is greatly affected by the parameters of the reservoir, an improved whale optimization algorithm is proposed to optimize these parameters. The optimized echo state network improves the forecasting effect. (d) The final forecasting results are obtained by adding the forecasting values of each component. (e) The performance of the developed forecasting model is verified by using two actual collected data sets of ultra-short-term wind speed and short-term wind speed. Compared with some state-of-the-art forecasting models, the comparison result curve between the forecasting value and actual value of wind speed, the forecasting error distribution, the histogram of the forecasting error distribution, the performance indicators, related statistical indicators, and Taylor diagram show that the developed forecasting model has higher prediction accuracy and is able to reflect the change laws of wind speed correctly.
Author Li, Feihong
Tian, Zhongda
Li, Hao
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Cites_doi 10.1007/s12559-020-09730-8
10.1016/j.enconman.2020.113500
10.1016/j.engappai.2020.103801
10.1016/j.enconman.2018.03.030
10.3390/en13071666
10.3390/en12020254
10.1016/j.neucom.2019.12.069
10.1049/iet-rpg.2018.5673
10.3390/en13071596
10.5370/JEET.2016.11.6.1548
10.1016/j.renene.2020.05.182
10.1007/s00500-019-04292-w
10.1016/j.enconman.2020.112956
10.1002/we.2422
10.3390/en12010069
10.1016/j.eswa.2020.113612
10.1016/j.physa.2019.123383
10.1016/j.renene.2017.11.089
10.1016/j.apenergy.2018.12.076
10.1007/s10489-020-01659-7
10.1016/j.ijepes.2019.105814
10.1016/j.enconman.2019.112099
10.4316/AECE.2017.01001
10.1016/j.artint.2019.103176
10.1016/j.enconman.2018.07.070
10.1016/j.jweia.2018.07.007
10.1007/s11063-020-10300-0
10.1016/j.rser.2016.10.056
10.1016/j.advengsoft.2016.01.008
10.1016/j.enconman.2020.113324
10.1007/s00500-019-04608-w
10.1016/j.apenergy.2015.10.145
10.1007/s00521-020-04849-z
10.1177/0309524X19849867
10.1080/00051144.2020.1811571
10.1016/j.renene.2019.04.154
10.1016/j.measurement.2020.107901
10.1016/j.enconman.2019.04.006
10.1109/JIOT.2019.2913176
10.1016/j.renene.2019.07.161
10.1016/j.esd.2014.02.004
10.1016/j.chaos.2017.03.018
10.1016/j.enconman.2017.05.063
10.1016/j.asoc.2020.106390
10.1016/j.enconman.2020.113234
10.1016/j.renene.2020.09.109
10.1016/j.renene.2020.10.126
10.1080/15567036.2018.1495782
10.1177/0142331219885273
10.1016/j.asoc.2020.106463
10.1016/j.engappai.2020.103573
10.1080/15435075.2020.1731816
10.1016/j.enconman.2020.113076
10.1016/j.renene.2018.07.060
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Keywords Improved whale optimization algorithm
Combination forecasting
Variational mode decomposition
Wind speed
Echo state network
Language English
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References Li, Xia, Liu, Li (b21) 2019; 13
Yu (b54) 2020; 17
Li, Zhang, Ji, Wu (b23) 2020; 6
Jiang, Liu, Zhao, Xin, Wu (b18) 2020; 220
Yu, Li, Zhang (b55) 2017; 148
Vinothkumar, Debba (b49) 2020; 24
Chen, Zeng, Lu, Weng (b6) 2019; 6
Moreno, da Silva, Mariani, Coelho (b32) 2020; 213
Zhao, Wei, Li, Zhang (b61) 2020; 13
Zhao, Guo, Su, Zhao, Xiao, Liu (b59) 2016; 162
Jiang, Liu, Peng, Zhao (b17) 2019; 200
Hu, Chen (b15) 2018; 173
Rana, Abd Latiff, Abdulhamid, Chiroma (b35) 2020; 32
Tian, Ren, Wang (b46) 2019; 41
Bokde, Feijoo, Al-Ansari, Tao, Yaseen (b3) 2020; 13
Wang, Wang, Yang (b52) 2019; 143
Liu, Li, Duan, Chen (b25) 2020; 224
Li, Yang, Wang (b22) 2019; 22
Natarajan, Nachimuthu (b33) 2020; 24
Zhou, Wang, Zhang (b62) 2020; 94
Chitsazan, Fadali, Trzynadlowski (b7) 2019; 131
Sim, Maaas, Lind (b39) 2019; 12
Moreno, Coelho (b30) 2018; 126
Hong, Satriani (b14) 2020; 2019
Hu, Wang, Tao (b16) 2021; 164
Li, Li (b19) 2018; 179
Liu, Cao, Zhang, Wang, Huang, Luo (b24) 2020; 121
Molina, Poyatos, Del Ser, Garcia, Hussain, Herrera (b29) 2020; 12
Tian (b43) 2020; 94
Do, Lee, Choi (b8) 2016; 11
da Silva, Ribeiro, Moreno, Mariani, Coelho (b38) 2021; 216
Bokde, Feijoo, Villanueva, Kulat (b4) 2019; 12
Mirjalili, Lewis (b28) 2016; 95
Wang, Han, Liu, Yan, Li (b51) 2019; 237
Tian, Li, Wang (b44) 2020; 23
Wang, Fu, Chen, Zhang, Xiong, Fang (b50) 2020; 222
He, Yu, Li, Xiang (b13) 2020; 205
Tian (b40) 2020
Zhang, Qin, Liu, Wang, Yao, Li, Li, Pei (b57) 2019; 192
Bedi, Toshniwal (b2) 2020; 93
Tian, Wang, Ren (b47) 2020; 44
Dupre, Drobinski, Alonzo, Badosa, Briard, Plougonven (b9) 2020; 145
Liu, Sun, Luo, Yang, Cao, Zhai (b26) 2020; 385
Mi, Zhao (b27) 2020; 216
Gai, Shen, Hu, Wang (b11) 2020; 162
Shao, Chao, Yang, Zhou (b37) 2017; 75
Navas, Prakash, Sasipraba (b34) 2020; 542
Zhang, Liu (b56) 2020; 226
Feng, Chen, Li, Luo (b10) 2020; 50
Shang, He, Song, Yang, Li, Chen (b36) 2020; 52
Tian (b42) 2020; 42
Zhang, Wang, Ni, Cheng, Lei (b58) 2017; 17
Moreno, Mariani, Coelho (b31) 2021; 164
Garcia, Huo, Prado, Bravo (b12) 2020; 161
Li, Li, Zhou, Wu, Shen, Liu, Gong (b20) 2020; 118
Tian (b41) 2020; 91
Zhao, Liu, Yu, Chang (b60) 2018; 164
Tian, Li, Wang, Sha (b45) 2017; 98
Yang, Chen (b53) 2019; 277
Chen, Li, Yang (b5) 2020; 158
Alam, Rehman, AI-Hadhrami, Meyer (b1) 2014; 22
Vidya, Janani (b48) 2020; 61
Jiang (10.1016/j.egyr.2021.02.002_b18) 2020; 220
Zhao (10.1016/j.egyr.2021.02.002_b60) 2018; 164
Chen (10.1016/j.egyr.2021.02.002_b6) 2019; 6
Tian (10.1016/j.egyr.2021.02.002_b45) 2017; 98
Yu (10.1016/j.egyr.2021.02.002_b55) 2017; 148
Sim (10.1016/j.egyr.2021.02.002_b39) 2019; 12
Alam (10.1016/j.egyr.2021.02.002_b1) 2014; 22
Jiang (10.1016/j.egyr.2021.02.002_b17) 2019; 200
Tian (10.1016/j.egyr.2021.02.002_b44) 2020; 23
Do (10.1016/j.egyr.2021.02.002_b8) 2016; 11
Bokde (10.1016/j.egyr.2021.02.002_b4) 2019; 12
Hong (10.1016/j.egyr.2021.02.002_b14) 2020; 2019
Feng (10.1016/j.egyr.2021.02.002_b10) 2020; 50
Gai (10.1016/j.egyr.2021.02.002_b11) 2020; 162
Hu (10.1016/j.egyr.2021.02.002_b16) 2021; 164
Rana (10.1016/j.egyr.2021.02.002_b35) 2020; 32
Tian (10.1016/j.egyr.2021.02.002_b46) 2019; 41
Moreno (10.1016/j.egyr.2021.02.002_b30) 2018; 126
Wang (10.1016/j.egyr.2021.02.002_b52) 2019; 143
Navas (10.1016/j.egyr.2021.02.002_b34) 2020; 542
Chen (10.1016/j.egyr.2021.02.002_b5) 2020; 158
Mi (10.1016/j.egyr.2021.02.002_b27) 2020; 216
Shang (10.1016/j.egyr.2021.02.002_b36) 2020; 52
Bokde (10.1016/j.egyr.2021.02.002_b3) 2020; 13
Moreno (10.1016/j.egyr.2021.02.002_b31) 2021; 164
Vinothkumar (10.1016/j.egyr.2021.02.002_b49) 2020; 24
Dupre (10.1016/j.egyr.2021.02.002_b9) 2020; 145
Liu (10.1016/j.egyr.2021.02.002_b25) 2020; 224
Liu (10.1016/j.egyr.2021.02.002_b26) 2020; 385
Tian (10.1016/j.egyr.2021.02.002_b47) 2020; 44
Mirjalili (10.1016/j.egyr.2021.02.002_b28) 2016; 95
Natarajan (10.1016/j.egyr.2021.02.002_b33) 2020; 24
Tian (10.1016/j.egyr.2021.02.002_b43) 2020; 94
Zhao (10.1016/j.egyr.2021.02.002_b59) 2016; 162
Li (10.1016/j.egyr.2021.02.002_b19) 2018; 179
Shao (10.1016/j.egyr.2021.02.002_b37) 2017; 75
Molina (10.1016/j.egyr.2021.02.002_b29) 2020; 12
Tian (10.1016/j.egyr.2021.02.002_b40) 2020
Zhao (10.1016/j.egyr.2021.02.002_b61) 2020; 13
Li (10.1016/j.egyr.2021.02.002_b22) 2019; 22
Li (10.1016/j.egyr.2021.02.002_b21) 2019; 13
Zhang (10.1016/j.egyr.2021.02.002_b57) 2019; 192
Tian (10.1016/j.egyr.2021.02.002_b41) 2020; 91
Wang (10.1016/j.egyr.2021.02.002_b50) 2020; 222
He (10.1016/j.egyr.2021.02.002_b13) 2020; 205
Zhou (10.1016/j.egyr.2021.02.002_b62) 2020; 94
Bedi (10.1016/j.egyr.2021.02.002_b2) 2020; 93
Chitsazan (10.1016/j.egyr.2021.02.002_b7) 2019; 131
Liu (10.1016/j.egyr.2021.02.002_b24) 2020; 121
Vidya (10.1016/j.egyr.2021.02.002_b48) 2020; 61
Wang (10.1016/j.egyr.2021.02.002_b51) 2019; 237
Zhang (10.1016/j.egyr.2021.02.002_b56) 2020; 226
Moreno (10.1016/j.egyr.2021.02.002_b32) 2020; 213
Hu (10.1016/j.egyr.2021.02.002_b15) 2018; 173
Garcia (10.1016/j.egyr.2021.02.002_b12) 2020; 161
Zhang (10.1016/j.egyr.2021.02.002_b58) 2017; 17
da Silva (10.1016/j.egyr.2021.02.002_b38) 2021; 216
Li (10.1016/j.egyr.2021.02.002_b20) 2020; 118
Yu (10.1016/j.egyr.2021.02.002_b54) 2020; 17
Yang (10.1016/j.egyr.2021.02.002_b53) 2019; 277
Tian (10.1016/j.egyr.2021.02.002_b42) 2020; 42
Li (10.1016/j.egyr.2021.02.002_b23) 2020; 6
References_xml – volume: 95
  start-page: 51
  year: 2016
  end-page: 67
  ident: b28
  article-title: The whale optimization algorithm
  publication-title: Adv. Eng. Softw.
– volume: 385
  start-page: 310
  year: 2020
  end-page: 318
  ident: b26
  article-title: Echo state network optimization using binary grey wolf algorithm
  publication-title: Neurocomputing
– volume: 126
  start-page: 736
  year: 2018
  end-page: 754
  ident: b30
  article-title: Wind speed forecasting approach based on singular spectrum analysis and adaptive neuro fuzzy inference system
  publication-title: Renew. Energy
– volume: 22
  start-page: 11575
  year: 2019
  end-page: 11581
  ident: b22
  article-title: Short-term wind speed forecasting based on improved ant colony algorithm for LSSVM
  publication-title: Clust. Comput. J. Netw. Softw. Tools Appl.
– volume: 216
  year: 2021
  ident: b38
  article-title: A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting
  publication-title: Energy
– volume: 237
  start-page: 1
  year: 2019
  end-page: 10
  ident: b51
  article-title: Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system
  publication-title: Appl. Energy
– volume: 22
  start-page: 34
  year: 2014
  end-page: 47
  ident: b1
  article-title: Extraction of the inherent nature of wind speed using wavelets and FFT
  publication-title: Energy Sustain. Dev.
– volume: 213
  year: 2020
  ident: b32
  article-title: Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network
  publication-title: Energy Convers. Manage.
– volume: 118
  year: 2020
  ident: b20
  article-title: An adaptive time-resolution method for ultra-short-term wind prediction
  publication-title: Int. J. Electr. Power Energy Syst.
– volume: 6
  start-page: 890
  year: 2020
  end-page: 900
  ident: b23
  article-title: Ultra-short term wind speed prediction using mathematical morphology decomposition and long short-term memory
  publication-title: CSEE J. Power Energy Syst.
– volume: 17
  start-page: 3
  year: 2017
  end-page: 8
  ident: b58
  article-title: Wind power prediction based on LS-SVM model with error correction
  publication-title: Adv. Electr. Comput. Eng.
– volume: 91
  year: 2020
  ident: b41
  article-title: Short-term wind speed prediction based on LMD and improved FA optimized combined kernel function LSSVM
  publication-title: Eng. Appl. Artif. Intell.
– volume: 143
  start-page: 1172
  year: 2019
  end-page: 1192
  ident: b52
  article-title: A novel non-linear combination system for short-term wind speed forecast
  publication-title: Renew. Energy
– volume: 32
  start-page: 16245
  year: 2020
  end-page: 16277
  ident: b35
  article-title: Whale optimization algorithm: a systematic review of contemporary applications, modifications and developments
  publication-title: Neural Comput. Appl.
– volume: 222
  year: 2020
  ident: b50
  article-title: A compound framework for wind speed forecasting based on comprehensive feature selection, quantile regression incorporated into convolutional simplified long short-term memory network and residual error correction
  publication-title: Energy Convers. Manage.
– volume: 162
  start-page: 808
  year: 2016
  end-page: 826
  ident: b59
  article-title: An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed
  publication-title: Appl. Energy
– volume: 145
  start-page: 2373
  year: 2020
  end-page: 2379
  ident: b9
  article-title: Sub-hourly forecasting of wind speed and wind energy
  publication-title: Renew. Energy
– volume: 24
  start-page: 5345
  year: 2020
  end-page: 5355
  ident: b49
  article-title: Hybrid wind speed prediction model based on recurrent long short-term memory neural network and support vector machine models
  publication-title: Soft Comput.
– volume: 13
  start-page: 1115
  year: 2019
  end-page: 1122
  ident: b21
  article-title: States prediction for solar power and wind speed using BBA-SVM
  publication-title: IET Renew. Power Gener.
– volume: 179
  start-page: 541
  year: 2018
  end-page: 557
  ident: b19
  article-title: Non-Gaussian non-stationary wind pressure forecasting based on the improved empirical wavelet transform
  publication-title: J. Wind Eng. Ind. Aerodyn.
– volume: 131
  start-page: 879
  year: 2019
  end-page: 889
  ident: b7
  article-title: Wind speed and wind direction forecasting using echo state network with nonlinear functions
  publication-title: Renew. Energy
– volume: 94
  year: 2020
  ident: b43
  article-title: Backtracking search optimization algorithm-based least square support vector machine and its applications
  publication-title: Eng. Appl. Artif. Intell.
– volume: 226
  year: 2020
  ident: b56
  article-title: Causal convolutional gated recurrent unit network with multiple decomposition methods for short-term wind speed forecasting
  publication-title: Energy Convers. Manage.
– volume: 224
  year: 2020
  ident: b25
  article-title: A review on multi-objective optimization framework in wind energy forecasting techniques and applications
  publication-title: Energy Convers. Manage.
– volume: 61
  start-page: 657
  year: 2020
  end-page: 669
  ident: b48
  article-title: Tabu search algorithm based general regression neural network for long term wind speed predictions
  publication-title: Automatika
– volume: 6
  start-page: 6997
  year: 2019
  end-page: 7010
  ident: b6
  article-title: A two-layer nonlinear combination method for short-term wind speed prediction based on ELM, ENN, and LSTM
  publication-title: IEEE Internet Things J.
– volume: 24
  start-page: 11441
  year: 2020
  end-page: 11458
  ident: b33
  article-title: New SVM kernel soft computing models for wind speed prediction in renewable energy applications
  publication-title: Soft Comput.
– volume: 13
  start-page: 1596
  year: 2020
  ident: b61
  article-title: A hybrid nonlinear forecasting strategy for short-term wind speed
  publication-title: Energies
– volume: 12
  start-page: 254
  year: 2019
  ident: b4
  article-title: A review on hybrid empirical mode decomposition models for wind speed and wind power prediction
  publication-title: Energies
– volume: 173
  start-page: 123
  year: 2018
  end-page: 142
  ident: b15
  article-title: A nonlinear hybrid wind speed forecasting model using LSTM network, hysteretic ELM and differential evolution algorithm
  publication-title: Energy Convers. Manage.
– volume: 42
  start-page: 1135
  year: 2020
  end-page: 1150
  ident: b42
  article-title: Kernel principal component analysis-based least squares support vector machine optimized by improved grey wolf optimization algorithm and application in dynamic liquid level forecasting of beam pump
  publication-title: Trans. Inst. Meas. Control
– volume: 13
  start-page: 1666
  year: 2020
  ident: b3
  article-title: The hybridization of ensemble empirical mode decomposition with forecasting models: application of short-term wind speed and power modeling
  publication-title: Energies
– volume: 98
  start-page: 158
  year: 2017
  end-page: 172
  ident: b45
  article-title: A prediction method based on wavelet transform and multiple models fusion for chaotic time series
  publication-title: Chaos Solitons Fractals
– volume: 164
  start-page: 1508
  year: 2021
  end-page: 1526
  ident: b31
  article-title: Hybrid multi-stage decomposition with parametric model applied to wind speed forecasting in Brazilian Northeast
  publication-title: Renew. Energy
– volume: 41
  start-page: 26
  year: 2019
  end-page: 46
  ident: b46
  article-title: Short-term wind speed prediction based on improved PSO algorithm optimized EM-ELM
  publication-title: Energy Sources Part A
– volume: 161
  start-page: 55
  year: 2020
  end-page: 64
  ident: b12
  article-title: Dynamic Bayesian temporal modeling and forecasting of short-term wind measurements
  publication-title: Renew. Energy
– volume: 200
  year: 2019
  ident: b17
  article-title: A novel wind speed prediction method based on robust local mean decomposition, group method of data handling and conditional kernel density estimation
  publication-title: Energy Convers. Manage.
– volume: 44
  start-page: 152
  year: 2020
  end-page: 167
  ident: b47
  article-title: Short-term wind speed forecasting based on autoregressive moving average with echo state network compensation
  publication-title: Wind Eng.
– volume: 220
  year: 2020
  ident: b18
  article-title: Short-term wind speed prediction using time varying filter-based empirical mode decomposition and group method of data handling-based hybrid model
  publication-title: Energy Convers. Manage.
– volume: 164
  start-page: 729
  year: 2021
  end-page: 751
  ident: b16
  article-title: Wind speed forecasting based on variational mode decomposition and improved echo state network
  publication-title: Renew. Energy
– volume: 162
  year: 2020
  ident: b11
  article-title: An integrated method based on hybrid grey wolf optimizer improved variational mode decomposition and deep neural network for fault diagnosis of rolling bearing
  publication-title: Measurement
– volume: 192
  start-page: 37
  year: 2019
  end-page: 51
  ident: b57
  article-title: Long short-term memory network based on neighborhood gates for processing complex causality in wind speed prediction
  publication-title: Energy Convers. Manage.
– volume: 121
  year: 2020
  ident: b24
  article-title: Short-term wind speed forecasting based on the Jaya-SVM model
  publication-title: Int. J. Electr. Power Energy
– volume: 158
  year: 2020
  ident: b5
  article-title: A whale optimization algorithm with chaos mechanism based on quasi-opposition for global optimization problems
  publication-title: Expert Syst. Appl.
– volume: 17
  start-page: 332
  year: 2020
  end-page: 348
  ident: b54
  article-title: Short-term wind speed forecasting based on random forest model combining ensemble empirical mode decomposition and improved harmony search algorithm
  publication-title: Int. J. Green Eenergy
– year: 2020
  ident: b40
  article-title: Chaotic characteristic analysis of short-term wind speed time series with different time scales
  publication-title: Energy Sources Part A
– volume: 12
  start-page: 897
  year: 2020
  end-page: 939
  ident: b29
  article-title: Comprehensive taxonomies of nature- and iio-inspired optimization: inspiration versus algorithmic behavior, critical analysis and recommendations
  publication-title: Cogn. Comput.
– volume: 23
  start-page: 177
  year: 2020
  end-page: 206
  ident: b44
  article-title: A prediction approach using ensemble empirical mode decomposition-permutation entropy and regularized extreme learning machine for short-term wind speed
  publication-title: Wind Energy
– volume: 11
  start-page: 1548
  year: 2016
  end-page: 1555
  ident: b8
  article-title: Hourly average wind speed simulation and forecast based on ARMA model in Jeju island, Korea
  publication-title: J. Electr. Eng. Technol.
– volume: 12
  start-page: 69
  year: 2019
  ident: b39
  article-title: Wind speed modeling by nested ARIMA processes
  publication-title: Energies
– volume: 542
  year: 2020
  ident: b34
  article-title: Artificial neural network based computing model for wind speed prediction: A case study of coimbatore, Tamil Nadu, India
  publication-title: Physica A
– volume: 164
  start-page: 560
  year: 2018
  end-page: 569
  ident: b60
  article-title: One-day-ahead probabilistic wind speed forecast based on optimized numerical weather prediction data
  publication-title: Energy Convers. Manage.
– volume: 75
  start-page: 123
  year: 2017
  end-page: 136
  ident: b37
  article-title: A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting
  publication-title: Renew. Sustain. Energy Rev.
– volume: 94
  year: 2020
  ident: b62
  article-title: A combined forecasting system based on modified multi-objective optimization and sub-model selection strategy for short-term wind speed
  publication-title: Appl. Soft Comput.
– volume: 277
  year: 2019
  ident: b53
  article-title: Representation learning with extreme learning machines and empirical mode decomposition for wind speed forecasting methods
  publication-title: Artificial Intelligence
– volume: 216
  year: 2020
  ident: b27
  article-title: Wind speed prediction based on singular spectrum analysis and neural network structural learning
  publication-title: Energy Convers. Manage.
– volume: 2019
  year: 2020
  ident: b14
  article-title: Day-ahead spatiotemporal wind speed forecasting using robust design-based deep learning neural network
  publication-title: Energy
– volume: 50
  start-page: 2503
  year: 2020
  end-page: 2522
  ident: b10
  article-title: A novel community detection method based on whale optimization algorithm with evolutionary population
  publication-title: Appl. Intell.
– volume: 205
  year: 2020
  ident: b13
  article-title: Ultra-short term wind prediction with wavelet transform, deep belief network and ensemble learning
  publication-title: Energy Convers. Manage.
– volume: 93
  year: 2020
  ident: b2
  article-title: Energy load time-series forecast using decomposition and autoencoder integrated memory network
  publication-title: Appl. Soft Comput.
– volume: 148
  start-page: 895
  year: 2017
  end-page: 904
  ident: b55
  article-title: An improved wavelet transform using singular spectrum analysis for wind speed forecasting based on elman neural network
  publication-title: Energy Convers. Manage.
– volume: 52
  start-page: 1207
  year: 2020
  end-page: 1232
  ident: b36
  article-title: A novel combined model for short-term electric load forecasting based on whale optimization algorithm
  publication-title: Neural Process. Lett.
– volume: 12
  start-page: 897
  issue: 5
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b29
  article-title: Comprehensive taxonomies of nature- and iio-inspired optimization: inspiration versus algorithmic behavior, critical analysis and recommendations
  publication-title: Cogn. Comput.
  doi: 10.1007/s12559-020-09730-8
– volume: 226
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b56
  article-title: Causal convolutional gated recurrent unit network with multiple decomposition methods for short-term wind speed forecasting
  publication-title: Energy Convers. Manage.
  doi: 10.1016/j.enconman.2020.113500
– volume: 94
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b43
  article-title: Backtracking search optimization algorithm-based least square support vector machine and its applications
  publication-title: Eng. Appl. Artif. Intell.
  doi: 10.1016/j.engappai.2020.103801
– volume: 164
  start-page: 560
  year: 2018
  ident: 10.1016/j.egyr.2021.02.002_b60
  article-title: One-day-ahead probabilistic wind speed forecast based on optimized numerical weather prediction data
  publication-title: Energy Convers. Manage.
  doi: 10.1016/j.enconman.2018.03.030
– volume: 13
  start-page: 1666
  issue: 7
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b3
  article-title: The hybridization of ensemble empirical mode decomposition with forecasting models: application of short-term wind speed and power modeling
  publication-title: Energies
  doi: 10.3390/en13071666
– volume: 213
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b32
  article-title: Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network
  publication-title: Energy Convers. Manage.
– volume: 216
  year: 2021
  ident: 10.1016/j.egyr.2021.02.002_b38
  article-title: A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting
  publication-title: Energy
– volume: 12
  start-page: 254
  issue: 2
  year: 2019
  ident: 10.1016/j.egyr.2021.02.002_b4
  article-title: A review on hybrid empirical mode decomposition models for wind speed and wind power prediction
  publication-title: Energies
  doi: 10.3390/en12020254
– volume: 385
  start-page: 310
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b26
  article-title: Echo state network optimization using binary grey wolf algorithm
  publication-title: Neurocomputing
  doi: 10.1016/j.neucom.2019.12.069
– volume: 13
  start-page: 1115
  issue: 7
  year: 2019
  ident: 10.1016/j.egyr.2021.02.002_b21
  article-title: States prediction for solar power and wind speed using BBA-SVM
  publication-title: IET Renew. Power Gener.
  doi: 10.1049/iet-rpg.2018.5673
– volume: 13
  start-page: 1596
  issue: 7
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b61
  article-title: A hybrid nonlinear forecasting strategy for short-term wind speed
  publication-title: Energies
  doi: 10.3390/en13071596
– volume: 11
  start-page: 1548
  issue: 6
  year: 2016
  ident: 10.1016/j.egyr.2021.02.002_b8
  article-title: Hourly average wind speed simulation and forecast based on ARMA model in Jeju island, Korea
  publication-title: J. Electr. Eng. Technol.
  doi: 10.5370/JEET.2016.11.6.1548
– volume: 161
  start-page: 55
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b12
  article-title: Dynamic Bayesian temporal modeling and forecasting of short-term wind measurements
  publication-title: Renew. Energy
  doi: 10.1016/j.renene.2020.05.182
– volume: 6
  start-page: 890
  issue: 4
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b23
  article-title: Ultra-short term wind speed prediction using mathematical morphology decomposition and long short-term memory
  publication-title: CSEE J. Power Energy Syst.
– volume: 24
  start-page: 5345
  issue: 7
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b49
  article-title: Hybrid wind speed prediction model based on recurrent long short-term memory neural network and support vector machine models
  publication-title: Soft Comput.
  doi: 10.1007/s00500-019-04292-w
– volume: 216
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b27
  article-title: Wind speed prediction based on singular spectrum analysis and neural network structural learning
  publication-title: Energy Convers. Manage.
  doi: 10.1016/j.enconman.2020.112956
– volume: 23
  start-page: 177
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b44
  article-title: A prediction approach using ensemble empirical mode decomposition-permutation entropy and regularized extreme learning machine for short-term wind speed
  publication-title: Wind Energy
  doi: 10.1002/we.2422
– volume: 12
  start-page: 69
  issue: 1
  year: 2019
  ident: 10.1016/j.egyr.2021.02.002_b39
  article-title: Wind speed modeling by nested ARIMA processes
  publication-title: Energies
  doi: 10.3390/en12010069
– volume: 158
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b5
  article-title: A whale optimization algorithm with chaos mechanism based on quasi-opposition for global optimization problems
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2020.113612
– volume: 542
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b34
  article-title: Artificial neural network based computing model for wind speed prediction: A case study of coimbatore, Tamil Nadu, India
  publication-title: Physica A
  doi: 10.1016/j.physa.2019.123383
– volume: 126
  start-page: 736
  year: 2018
  ident: 10.1016/j.egyr.2021.02.002_b30
  article-title: Wind speed forecasting approach based on singular spectrum analysis and adaptive neuro fuzzy inference system
  publication-title: Renew. Energy
  doi: 10.1016/j.renene.2017.11.089
– volume: 237
  start-page: 1
  year: 2019
  ident: 10.1016/j.egyr.2021.02.002_b51
  article-title: Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system
  publication-title: Appl. Energy
  doi: 10.1016/j.apenergy.2018.12.076
– volume: 50
  start-page: 2503
  issue: 8
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b10
  article-title: A novel community detection method based on whale optimization algorithm with evolutionary population
  publication-title: Appl. Intell.
  doi: 10.1007/s10489-020-01659-7
– volume: 118
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b20
  article-title: An adaptive time-resolution method for ultra-short-term wind prediction
  publication-title: Int. J. Electr. Power Energy Syst.
  doi: 10.1016/j.ijepes.2019.105814
– volume: 200
  year: 2019
  ident: 10.1016/j.egyr.2021.02.002_b17
  article-title: A novel wind speed prediction method based on robust local mean decomposition, group method of data handling and conditional kernel density estimation
  publication-title: Energy Convers. Manage.
  doi: 10.1016/j.enconman.2019.112099
– volume: 17
  start-page: 3
  issue: 1
  year: 2017
  ident: 10.1016/j.egyr.2021.02.002_b58
  article-title: Wind power prediction based on LS-SVM model with error correction
  publication-title: Adv. Electr. Comput. Eng.
  doi: 10.4316/AECE.2017.01001
– volume: 277
  year: 2019
  ident: 10.1016/j.egyr.2021.02.002_b53
  article-title: Representation learning with extreme learning machines and empirical mode decomposition for wind speed forecasting methods
  publication-title: Artificial Intelligence
  doi: 10.1016/j.artint.2019.103176
– volume: 173
  start-page: 123
  year: 2018
  ident: 10.1016/j.egyr.2021.02.002_b15
  article-title: A nonlinear hybrid wind speed forecasting model using LSTM network, hysteretic ELM and differential evolution algorithm
  publication-title: Energy Convers. Manage.
  doi: 10.1016/j.enconman.2018.07.070
– volume: 179
  start-page: 541
  year: 2018
  ident: 10.1016/j.egyr.2021.02.002_b19
  article-title: Non-Gaussian non-stationary wind pressure forecasting based on the improved empirical wavelet transform
  publication-title: J. Wind Eng. Ind. Aerodyn.
  doi: 10.1016/j.jweia.2018.07.007
– volume: 52
  start-page: 1207
  issue: 2
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b36
  article-title: A novel combined model for short-term electric load forecasting based on whale optimization algorithm
  publication-title: Neural Process. Lett.
  doi: 10.1007/s11063-020-10300-0
– volume: 75
  start-page: 123
  year: 2017
  ident: 10.1016/j.egyr.2021.02.002_b37
  article-title: A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting
  publication-title: Renew. Sustain. Energy Rev.
  doi: 10.1016/j.rser.2016.10.056
– volume: 121
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b24
  article-title: Short-term wind speed forecasting based on the Jaya-SVM model
  publication-title: Int. J. Electr. Power Energy
– volume: 205
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b13
  article-title: Ultra-short term wind prediction with wavelet transform, deep belief network and ensemble learning
  publication-title: Energy Convers. Manage.
– volume: 95
  start-page: 51
  year: 2016
  ident: 10.1016/j.egyr.2021.02.002_b28
  article-title: The whale optimization algorithm
  publication-title: Adv. Eng. Softw.
  doi: 10.1016/j.advengsoft.2016.01.008
– volume: 224
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b25
  article-title: A review on multi-objective optimization framework in wind energy forecasting techniques and applications
  publication-title: Energy Convers. Manage.
  doi: 10.1016/j.enconman.2020.113324
– volume: 24
  start-page: 11441
  issue: 15
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b33
  article-title: New SVM kernel soft computing models for wind speed prediction in renewable energy applications
  publication-title: Soft Comput.
  doi: 10.1007/s00500-019-04608-w
– volume: 162
  start-page: 808
  year: 2016
  ident: 10.1016/j.egyr.2021.02.002_b59
  article-title: An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed
  publication-title: Appl. Energy
  doi: 10.1016/j.apenergy.2015.10.145
– volume: 32
  start-page: 16245
  issue: 20
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b35
  article-title: Whale optimization algorithm: a systematic review of contemporary applications, modifications and developments
  publication-title: Neural Comput. Appl.
  doi: 10.1007/s00521-020-04849-z
– volume: 44
  start-page: 152
  issue: 2
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b47
  article-title: Short-term wind speed forecasting based on autoregressive moving average with echo state network compensation
  publication-title: Wind Eng.
  doi: 10.1177/0309524X19849867
– year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b40
  article-title: Chaotic characteristic analysis of short-term wind speed time series with different time scales
  publication-title: Energy Sources Part A
– volume: 61
  start-page: 657
  issue: 4
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b48
  article-title: Tabu search algorithm based general regression neural network for long term wind speed predictions
  publication-title: Automatika
  doi: 10.1080/00051144.2020.1811571
– volume: 143
  start-page: 1172
  year: 2019
  ident: 10.1016/j.egyr.2021.02.002_b52
  article-title: A novel non-linear combination system for short-term wind speed forecast
  publication-title: Renew. Energy
  doi: 10.1016/j.renene.2019.04.154
– volume: 162
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b11
  article-title: An integrated method based on hybrid grey wolf optimizer improved variational mode decomposition and deep neural network for fault diagnosis of rolling bearing
  publication-title: Measurement
  doi: 10.1016/j.measurement.2020.107901
– volume: 192
  start-page: 37
  year: 2019
  ident: 10.1016/j.egyr.2021.02.002_b57
  article-title: Long short-term memory network based on neighborhood gates for processing complex causality in wind speed prediction
  publication-title: Energy Convers. Manage.
  doi: 10.1016/j.enconman.2019.04.006
– volume: 6
  start-page: 6997
  issue: 4
  year: 2019
  ident: 10.1016/j.egyr.2021.02.002_b6
  article-title: A two-layer nonlinear combination method for short-term wind speed prediction based on ELM, ENN, and LSTM
  publication-title: IEEE Internet Things J.
  doi: 10.1109/JIOT.2019.2913176
– volume: 145
  start-page: 2373
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b9
  article-title: Sub-hourly forecasting of wind speed and wind energy
  publication-title: Renew. Energy
  doi: 10.1016/j.renene.2019.07.161
– volume: 22
  start-page: 34
  year: 2014
  ident: 10.1016/j.egyr.2021.02.002_b1
  article-title: Extraction of the inherent nature of wind speed using wavelets and FFT
  publication-title: Energy Sustain. Dev.
  doi: 10.1016/j.esd.2014.02.004
– volume: 22
  start-page: 11575
  year: 2019
  ident: 10.1016/j.egyr.2021.02.002_b22
  article-title: Short-term wind speed forecasting based on improved ant colony algorithm for LSSVM
  publication-title: Clust. Comput. J. Netw. Softw. Tools Appl.
– volume: 98
  start-page: 158
  year: 2017
  ident: 10.1016/j.egyr.2021.02.002_b45
  article-title: A prediction method based on wavelet transform and multiple models fusion for chaotic time series
  publication-title: Chaos Solitons Fractals
  doi: 10.1016/j.chaos.2017.03.018
– volume: 148
  start-page: 895
  year: 2017
  ident: 10.1016/j.egyr.2021.02.002_b55
  article-title: An improved wavelet transform using singular spectrum analysis for wind speed forecasting based on elman neural network
  publication-title: Energy Convers. Manage.
  doi: 10.1016/j.enconman.2017.05.063
– volume: 93
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b2
  article-title: Energy load time-series forecast using decomposition and autoencoder integrated memory network
  publication-title: Appl. Soft Comput.
  doi: 10.1016/j.asoc.2020.106390
– volume: 222
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b50
  article-title: A compound framework for wind speed forecasting based on comprehensive feature selection, quantile regression incorporated into convolutional simplified long short-term memory network and residual error correction
  publication-title: Energy Convers. Manage.
  doi: 10.1016/j.enconman.2020.113234
– volume: 164
  start-page: 729
  year: 2021
  ident: 10.1016/j.egyr.2021.02.002_b16
  article-title: Wind speed forecasting based on variational mode decomposition and improved echo state network
  publication-title: Renew. Energy
  doi: 10.1016/j.renene.2020.09.109
– volume: 164
  start-page: 1508
  year: 2021
  ident: 10.1016/j.egyr.2021.02.002_b31
  article-title: Hybrid multi-stage decomposition with parametric model applied to wind speed forecasting in Brazilian Northeast
  publication-title: Renew. Energy
  doi: 10.1016/j.renene.2020.10.126
– volume: 41
  start-page: 26
  issue: 1
  year: 2019
  ident: 10.1016/j.egyr.2021.02.002_b46
  article-title: Short-term wind speed prediction based on improved PSO algorithm optimized EM-ELM
  publication-title: Energy Sources Part A
  doi: 10.1080/15567036.2018.1495782
– volume: 42
  start-page: 1135
  issue: 6
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b42
  article-title: Kernel principal component analysis-based least squares support vector machine optimized by improved grey wolf optimization algorithm and application in dynamic liquid level forecasting of beam pump
  publication-title: Trans. Inst. Meas. Control
  doi: 10.1177/0142331219885273
– volume: 94
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b62
  article-title: A combined forecasting system based on modified multi-objective optimization and sub-model selection strategy for short-term wind speed
  publication-title: Appl. Soft Comput.
  doi: 10.1016/j.asoc.2020.106463
– volume: 91
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b41
  article-title: Short-term wind speed prediction based on LMD and improved FA optimized combined kernel function LSSVM
  publication-title: Eng. Appl. Artif. Intell.
  doi: 10.1016/j.engappai.2020.103573
– volume: 17
  start-page: 332
  issue: 5
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b54
  article-title: Short-term wind speed forecasting based on random forest model combining ensemble empirical mode decomposition and improved harmony search algorithm
  publication-title: Int. J. Green Eenergy
  doi: 10.1080/15435075.2020.1731816
– volume: 220
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b18
  article-title: Short-term wind speed prediction using time varying filter-based empirical mode decomposition and group method of data handling-based hybrid model
  publication-title: Energy Convers. Manage.
  doi: 10.1016/j.enconman.2020.113076
– volume: 2019
  year: 2020
  ident: 10.1016/j.egyr.2021.02.002_b14
  article-title: Day-ahead spatiotemporal wind speed forecasting using robust design-based deep learning neural network
  publication-title: Energy
– volume: 131
  start-page: 879
  year: 2019
  ident: 10.1016/j.egyr.2021.02.002_b7
  article-title: Wind speed and wind direction forecasting using echo state network with nonlinear functions
  publication-title: Renew. Energy
  doi: 10.1016/j.renene.2018.07.060
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Snippet Due to the intermittent, fluctuating and random characteristics of wind system, the output of wind power will become unstable with the change of wind, which...
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SubjectTerms Combination forecasting
Echo state network
Improved whale optimization algorithm
Variational mode decomposition
Wind speed
Title A combination forecasting model of wind speed based on decomposition
URI https://dx.doi.org/10.1016/j.egyr.2021.02.002
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