A novel combined model based on advanced optimization algorithm, and deep learning model for abnormal wind speed identification and reconstruction
When the state of the sensors of the wind turbine, especially the anemometer, is abnormal, it will affect the correctness of other parameters of the whole power system. To recognize and reconstruct the abnormal data accurately and efficiently, this study proposes an improved complete ensemble empiri...
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| Veröffentlicht in: | Energy (Oxford) Jg. 312; S. 133510 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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15.12.2024
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| ISSN: | 0360-5442 |
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| Abstract | When the state of the sensors of the wind turbine, especially the anemometer, is abnormal, it will affect the correctness of other parameters of the whole power system. To recognize and reconstruct the abnormal data accurately and efficiently, this study proposes an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), extreme learning machine (ELM), crazy improve butterfly optimization algorithm (CIBOA), and bi-directional long short-term memory (BILSTM). In this system, the ICEEMDAN is utilized to decompose the original wind speed sequence, and then the ELM is employed as the initial prediction engine to extract the features of each sub-sequence to achieve the preliminary prediction outcomes. The CIBOA is employed to optimize the BILSTM model parameters. To further explore the unsteady features in the wind speed series, the residual results of the preliminary prediction are modeled using BILSTM, and the predicted residuals and preliminary results are integrated to obtain the final reconstructed values. In addition, the combined model is discussed in detail employing six assessment indicators, improvements of the reconstruction model, Diebold-Mariano test, operation time, and sensitivity analysis. The results indicate that the Pearson correlation coefficient (PCC) values of the proposed model are 0.9986, 0.9978, and 0.9979, respectively. It is concluded that the proposed hybrid reconstruction model accurately identifies and reconstructs abnormal wind speed, which provides a new technique for the reasonable utilization of wind energy.
•Combines signal decomposition, multiple reconstruction modeling, intelligent optimization, and deep learning.•Initial and residual forecasts are applied simultaneously to identify and reconstruct abnormal wind speeds.•Improved intelligent optimization algorithm for parameter optimization.•Developed an abnormal wind speed identification and reconstruction system. |
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| AbstractList | When the state of the sensors of the wind turbine, especially the anemometer, is abnormal, it will affect the correctness of other parameters of the whole power system. To recognize and reconstruct the abnormal data accurately and efficiently, this study proposes an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), extreme learning machine (ELM), crazy improve butterfly optimization algorithm (CIBOA), and bi-directional long short-term memory (BILSTM). In this system, the ICEEMDAN is utilized to decompose the original wind speed sequence, and then the ELM is employed as the initial prediction engine to extract the features of each sub-sequence to achieve the preliminary prediction outcomes. The CIBOA is employed to optimize the BILSTM model parameters. To further explore the unsteady features in the wind speed series, the residual results of the preliminary prediction are modeled using BILSTM, and the predicted residuals and preliminary results are integrated to obtain the final reconstructed values. In addition, the combined model is discussed in detail employing six assessment indicators, improvements of the reconstruction model, Diebold-Mariano test, operation time, and sensitivity analysis. The results indicate that the Pearson correlation coefficient (PCC) values of the proposed model are 0.9986, 0.9978, and 0.9979, respectively. It is concluded that the proposed hybrid reconstruction model accurately identifies and reconstructs abnormal wind speed, which provides a new technique for the reasonable utilization of wind energy. When the state of the sensors of the wind turbine, especially the anemometer, is abnormal, it will affect the correctness of other parameters of the whole power system. To recognize and reconstruct the abnormal data accurately and efficiently, this study proposes an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), extreme learning machine (ELM), crazy improve butterfly optimization algorithm (CIBOA), and bi-directional long short-term memory (BILSTM). In this system, the ICEEMDAN is utilized to decompose the original wind speed sequence, and then the ELM is employed as the initial prediction engine to extract the features of each sub-sequence to achieve the preliminary prediction outcomes. The CIBOA is employed to optimize the BILSTM model parameters. To further explore the unsteady features in the wind speed series, the residual results of the preliminary prediction are modeled using BILSTM, and the predicted residuals and preliminary results are integrated to obtain the final reconstructed values. In addition, the combined model is discussed in detail employing six assessment indicators, improvements of the reconstruction model, Diebold-Mariano test, operation time, and sensitivity analysis. The results indicate that the Pearson correlation coefficient (PCC) values of the proposed model are 0.9986, 0.9978, and 0.9979, respectively. It is concluded that the proposed hybrid reconstruction model accurately identifies and reconstructs abnormal wind speed, which provides a new technique for the reasonable utilization of wind energy. •Combines signal decomposition, multiple reconstruction modeling, intelligent optimization, and deep learning.•Initial and residual forecasts are applied simultaneously to identify and reconstruct abnormal wind speeds.•Improved intelligent optimization algorithm for parameter optimization.•Developed an abnormal wind speed identification and reconstruction system. |
| ArticleNumber | 133510 |
| Author | Zhou, Ling Zhao, Qiancheng Zeng, Bing Yang, Tianlong Shi, Zhaoyao Zhu, Anfeng |
| Author_xml | – sequence: 1 givenname: Anfeng surname: Zhu fullname: Zhu, Anfeng organization: Engineering Research Center of Hunan Province for the Mining and Utilization of Wind Turbines Operation Data, Hunan University of Science and Technology, Xiangtan, 411201, China – sequence: 2 givenname: Qiancheng surname: Zhao fullname: Zhao, Qiancheng email: qczhao@hnust.edu.cn organization: Engineering Research Center of Hunan Province for the Mining and Utilization of Wind Turbines Operation Data, Hunan University of Science and Technology, Xiangtan, 411201, China – sequence: 3 givenname: Zhaoyao surname: Shi fullname: Shi, Zhaoyao organization: Engineering Research Center of Hunan Province for the Mining and Utilization of Wind Turbines Operation Data, Hunan University of Science and Technology, Xiangtan, 411201, China – sequence: 4 givenname: Tianlong surname: Yang fullname: Yang, Tianlong organization: Engineering Research Center of Hunan Province for the Mining and Utilization of Wind Turbines Operation Data, Hunan University of Science and Technology, Xiangtan, 411201, China – sequence: 5 givenname: Ling surname: Zhou fullname: Zhou, Ling organization: Engineering Research Center of Hunan Province for the Mining and Utilization of Wind Turbines Operation Data, Hunan University of Science and Technology, Xiangtan, 411201, China – sequence: 6 givenname: Bing surname: Zeng fullname: Zeng, Bing organization: XEMC Wind Power Co., Ltd., Xiangtan, 411102, China |
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| Cites_doi | 10.1016/j.renene.2019.12.047 10.1016/j.ymssp.2022.109285 10.1016/j.renene.2020.08.077 10.1007/s13369-020-05311-x 10.1016/j.solener.2004.09.013 10.1016/j.egyr.2024.01.058 10.1016/j.atmosres.2023.107032 10.1016/j.energy.2022.123785 10.1016/j.renene.2018.02.092 10.1016/j.enconman.2019.02.086 10.5194/wes-9-1431-2024 10.1016/j.apenergy.2018.02.070 10.1016/j.enconman.2016.02.013 10.1002/stc.2783 10.1016/j.matcom.2022.08.020 10.1016/j.eswa.2022.117847 10.1016/j.compeleceng.2022.108538 10.1175/MWR-D-17-0198.1 10.1016/j.enconman.2021.115102 10.1016/j.renene.2016.03.103 10.1016/j.apenergy.2022.118777 10.3390/en9010011 10.1016/j.energy.2022.124249 10.1016/j.apenergy.2020.115561 10.1016/j.measurement.2023.113643 10.1016/j.apenergy.2019.02.015 10.1016/j.apenergy.2019.04.188 10.1016/j.energy.2022.123848 10.1016/j.egyr.2023.05.181 10.1002/jnm.2040 10.3390/en15093055 10.1016/j.compeleceng.2024.109074 10.1016/j.energy.2022.123761 10.1016/j.energy.2023.127865 10.1016/j.energy.2023.128947 10.1016/j.enconman.2017.11.067 10.1016/j.apenergy.2017.04.017 10.1198/073500102753410444 10.1016/j.enconman.2018.07.070 |
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| Keywords | Combination strategy Wind speed reconstruction Improved optimization algorithms Deep learning algorithm |
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| References | Zhang, Wang (bib44) 2023; 278 Liu, Liu (bib32) 2023; 222 De, Kar, Mandal, Ghoshal (bib40) 2015; 28 Wang, Zhang, Wu, Wang (bib29) 2016; 94 Liu, Chen (bib2) 2022; 249 Wang, Tang, Zhao (bib22) 2024; 238 Leme Beu, Landulfo (bib42) 2024; 9 Li, Song, Wang, Wang, Jia (bib9) 2022; 251 Hua, Zhang, Peng, Ji, Nazir (bib20) 2022; 252 Meng, Ge, Yin, Chen (bib3) 2016; 114 Zhang, Zhang, Wang, Niu (bib4) 2020; 277 Gwec, Global Wind Energy Council (GWEC) 2024. Movahed, Bidgoly, Manesh, Mirzaei (bib15) 2021; 127 Liu, Mi, Li (bib36) 2018; 123 Yang, Astitha, Delle Monache, Alessandrini (bib11) 2018; 146 Li, Zou (bib30) 2022; 47 Hu, Heng, Wen, Zhao (bib17) 2020; 162 Song, Wang, Lu (bib31) 2018; 215 Gao, Guo, Mei, Sha, Guo, Sun (bib28) 2023; 9 Li, Yu, Liu (bib38) 2023; 204 Zhao, Guo, Xiao, Wang, Chi, Guo (bib7) 2017; 197 Lin, Li (bib16) 2021; 28 Zhang, Pan, Chen (bib19) 2020; 156 Emeksiz, Tan (bib34) 2022; 249 Zhu, Zhao, Yang, Zhou, Zeng (bib5) 2023; 105 Fu, Wang, Li, Tan (bib33) 2019; 187 Wang, Zhang, Xu, Song, Fan (bib39) 2020; 37 Zhu, Zhao, Wang, Zhou (bib24) 2022; 15 Li, Zhu, Li (bib18) 2022; 178 Tian, Wang (bib1) 2022; 254 Liu, Lou, Yan, Qi, Jin, Yu, Yang, Zhao, Xia (bib13) 2023; 295 Han, Mi, Shen, Cai, Liu, Li, Xu (bib10) 2022; 312 Zhu, Zhao, Yang, Zhou, Zeng (bib27) 2024; 114 Bali, Kumar, Gangwar (bib41) 2019 Sun, Wang, Yan (bib23) 2024; 11 Liang, Lin, Lu (bib35) 2022; 206 Sun, Wang (bib25) 2018; 157 Wang, Zhang, Wu, Wang (bib26) 2016; 94 Yang, Guo, Huang (bib12) 2023; 282 Liang, Chai, Sun, Tan (bib43) 2022; 250 Lucheroni, Boland, Ragno (bib8) 2019; 239 Zhu, Li, Sun, Nie, Yao, Zhao (bib21) 2015; 9 Hu, Chen (bib37) 2018; 173 Tang, Li, Gong (bib46) 2008; 30 Torres, Garcia, De Blas, De Francisco (bib14) 2005; 79 Diebold, Mariano (bib45) 2002; 20 Liu (10.1016/j.energy.2024.133510_bib13) 2023; 295 Leme Beu (10.1016/j.energy.2024.133510_bib42) 2024; 9 Tang (10.1016/j.energy.2024.133510_bib46) 2008; 30 Hua (10.1016/j.energy.2024.133510_bib20) 2022; 252 Zhu (10.1016/j.energy.2024.133510_bib24) 2022; 15 Movahed (10.1016/j.energy.2024.133510_bib15) 2021; 127 Liu (10.1016/j.energy.2024.133510_bib32) 2023; 222 De (10.1016/j.energy.2024.133510_bib40) 2015; 28 Gao (10.1016/j.energy.2024.133510_bib28) 2023; 9 Liu (10.1016/j.energy.2024.133510_bib2) 2022; 249 Zhang (10.1016/j.energy.2024.133510_bib19) 2020; 156 Wang (10.1016/j.energy.2024.133510_bib26) 2016; 94 Hu (10.1016/j.energy.2024.133510_bib37) 2018; 173 Wang (10.1016/j.energy.2024.133510_bib22) 2024; 238 Wang (10.1016/j.energy.2024.133510_bib39) 2020; 37 Bali (10.1016/j.energy.2024.133510_bib41) 2019 Sun (10.1016/j.energy.2024.133510_bib23) 2024; 11 Zhu (10.1016/j.energy.2024.133510_bib27) 2024; 114 Li (10.1016/j.energy.2024.133510_bib38) 2023; 204 Meng (10.1016/j.energy.2024.133510_bib3) 2016; 114 Li (10.1016/j.energy.2024.133510_bib18) 2022; 178 Zhang (10.1016/j.energy.2024.133510_bib4) 2020; 277 Lucheroni (10.1016/j.energy.2024.133510_bib8) 2019; 239 Wang (10.1016/j.energy.2024.133510_bib29) 2016; 94 Song (10.1016/j.energy.2024.133510_bib31) 2018; 215 Zhang (10.1016/j.energy.2024.133510_bib44) 2023; 278 Fu (10.1016/j.energy.2024.133510_bib33) 2019; 187 Liang (10.1016/j.energy.2024.133510_bib43) 2022; 250 Hu (10.1016/j.energy.2024.133510_bib17) 2020; 162 Sun (10.1016/j.energy.2024.133510_bib25) 2018; 157 Emeksiz (10.1016/j.energy.2024.133510_bib34) 2022; 249 Yang (10.1016/j.energy.2024.133510_bib11) 2018; 146 Liu (10.1016/j.energy.2024.133510_bib36) 2018; 123 Li (10.1016/j.energy.2024.133510_bib9) 2022; 251 Tian (10.1016/j.energy.2024.133510_bib1) 2022; 254 Liang (10.1016/j.energy.2024.133510_bib35) 2022; 206 10.1016/j.energy.2024.133510_bib6 Li (10.1016/j.energy.2024.133510_bib30) 2022; 47 Han (10.1016/j.energy.2024.133510_bib10) 2022; 312 Lin (10.1016/j.energy.2024.133510_bib16) 2021; 28 Torres (10.1016/j.energy.2024.133510_bib14) 2005; 79 Zhao (10.1016/j.energy.2024.133510_bib7) 2017; 197 Zhu (10.1016/j.energy.2024.133510_bib5) 2023; 105 Yang (10.1016/j.energy.2024.133510_bib12) 2023; 282 Zhu (10.1016/j.energy.2024.133510_bib21) 2015; 9 Diebold (10.1016/j.energy.2024.133510_bib45) 2002; 20 |
| References_xml | – volume: 277 year: 2020 ident: bib4 article-title: Hybrid system based on a multi-objective optimization and kernel approximation for multi-scale wind speed forecasting publication-title: Appl Energy – start-page: 426 year: 2019 end-page: 431 ident: bib41 article-title: Deep learning based wind speed forecasting-A review publication-title: 9th international conference on cloud computing, data science & engineering confluence IEEE – volume: 28 year: 2021 ident: bib16 article-title: Nonstationary wind speed data reconstruction based on secondary correction of statistical characteristics publication-title: Struct Control Health Monit – volume: 178 year: 2022 ident: bib18 article-title: Comparative analysis of BPNN, SVR, LSTM, Random Forest, and LSTM-SVR for conditional simulation of non-Gaussian measured fluctuating wind pressures publication-title: Mech Syst Signal Process – volume: 79 start-page: 65 year: 2005 end-page: 77 ident: bib14 article-title: Forecast of hourly average wind speed with ARMA models in Navarre (Spain) publication-title: Sol Energy – volume: 47 start-page: 3669 year: 2022 end-page: 3682 ident: bib30 article-title: Short-term wind power prediction based on data reconstruction and improved extreme learning machine publication-title: Arabian J Sci Eng – volume: 295 year: 2023 ident: bib13 article-title: Deep-learning post-processing of short-term station precipitation based on NWP forecasts publication-title: Atmos Res – volume: 252 year: 2022 ident: bib20 article-title: Integrated framework of extreme learning machine (ELM) based on improved atom search optimization for short-term wind speed prediction publication-title: Energy Convers Manag – volume: 157 start-page: 1 year: 2018 end-page: 12 ident: bib25 article-title: Short-term wind speed forecasting based on fast ensemble empirical mode decomposition, phase space reconstruction, sample entropy and improved back-propagation neural network publication-title: Energy Convers Manag – volume: 30 start-page: 89 year: 2008 end-page: 92 ident: bib46 article-title: Research on properties of combination forecasting model based on absolute of grey incidence publication-title: Syst Eng Electron – volume: 37 start-page: 3276 year: 2020 end-page: 3280 ident: bib39 article-title: Crazy butterfly algorithm based on adaptive perturbation publication-title: Appl Res Comput – volume: 28 start-page: 593 year: 2015 end-page: 609 ident: bib40 article-title: Optimal analog active filter design using craziness‐based particle swarm optimization algorithm publication-title: Int J Numer Model Electron Network Dev Field – volume: 250 year: 2022 ident: bib43 article-title: Wind speed prediction based on multi-variable Capsnet-BILSTM-MOHHO for WPCCC publication-title: Energy – volume: 20 start-page: 134 year: 2002 end-page: 144 ident: bib45 article-title: Comparing predictive accuracy publication-title: J Bus Econ Statistics – volume: 197 start-page: 183 year: 2017 end-page: 202 ident: bib7 article-title: Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method publication-title: Applied energy – volume: 162 start-page: 1208 year: 2020 end-page: 1226 ident: bib17 article-title: Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm publication-title: Renew Energy – volume: 187 start-page: 356 year: 2019 end-page: 377 ident: bib33 article-title: Multi-step short-term wind speed forecasting approach based on multi-scale dominant ingredient chaotic analysis, improved hybrid GWO-SCA optimization and ELM publication-title: Energy Convers Manag – volume: 282 year: 2023 ident: bib12 article-title: Wind power ultra-short-term prediction method based on NWP wind speed correction and double clustering division of transitional weather process publication-title: Energy – volume: 9 start-page: 1431 year: 2024 end-page: 1450 ident: bib42 article-title: Machine-learning-based estimate of the wind speed over complex terrain using the long short-term memory (LSTM) recurrent neural network publication-title: Wind Energy Science – volume: 15 start-page: 3055 year: 2022 ident: bib24 article-title: Ultra-short-term wind power combined prediction based on complementary ensemble empirical mode decomposition, whale optimization algorithm, and Elman network publication-title: Energies – volume: 11 start-page: 2127 year: 2024 end-page: 2140 ident: bib23 article-title: Ultra-short-term wind speed prediction based on TCN-MCM-EKF publication-title: Energy Rep – volume: 123 start-page: 694 year: 2018 end-page: 705 ident: bib36 article-title: An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm publication-title: Renew Energy – volume: 105 year: 2023 ident: bib5 article-title: Condition monitoring of wind turbine based on deep learning networks and kernel principal component analysis publication-title: Comput Electr Eng – volume: 94 start-page: 629 year: 2016 end-page: 636 ident: bib26 article-title: Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method publication-title: Renew Energy – volume: 215 start-page: 643 year: 2018 end-page: 658 ident: bib31 article-title: A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting publication-title: Applied energy – volume: 238 year: 2024 ident: bib22 article-title: Robust multi-step wind speed forecasting based on a graph-based data reconstruction deep learning method publication-title: Expert Syst Appl – volume: 156 start-page: 1373 year: 2020 end-page: 1388 ident: bib19 article-title: Short-term wind speed prediction model based on GA-ANN improved by VMD publication-title: Renew Energy – volume: 249 year: 2022 ident: bib34 article-title: Wind speed estimation using novelty hybrid adaptive estimation model based on decomposition and deep learning methods (ICEEMDAN-CNN) publication-title: Energy – volume: 173 start-page: 123 year: 2018 end-page: 142 ident: bib37 article-title: A nonlinear hybrid wind speed forecasting model using LSTM network, hysteretic ELM and Differential Evolution algorithm publication-title: Energy Convers Manag – reference: Gwec, Global Wind Energy Council (GWEC) 2024. – volume: 278 year: 2023 ident: bib44 article-title: Multi-head attention-based probabilistic CNN-BiLSTM for day-ahead wind speed forecasting publication-title: Energy – volume: 204 start-page: 498 year: 2023 end-page: 528 ident: bib38 article-title: An opposition-based butterfly optimization algorithm with adaptive elite mutation in solving complex high-dimensional optimization problems publication-title: Math Comput Simulat – volume: 114 start-page: 75 year: 2016 end-page: 88 ident: bib3 article-title: Wind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithm publication-title: Energy Convers Manag – volume: 146 start-page: 4057 year: 2018 end-page: 4077 ident: bib11 article-title: An analog technique to improve storm wind speed prediction using a dual NWP model approach publication-title: Mon Weather Rev – volume: 9 start-page: 335 year: 2023 end-page: 344 ident: bib28 article-title: Short-term wind power forecasting based on SSA-VMD-LSTM publication-title: Energy Rep – volume: 9 start-page: 11 year: 2015 ident: bib21 article-title: A power prediction method for photovoltaic power plant based on wavelet decomposition and artificial neural networks publication-title: Energies – volume: 251 year: 2022 ident: bib9 article-title: A novel offshore wind farm typhoon wind speed prediction model based on PSO–Bi-LSTM improved by VMD publication-title: Energy – volume: 127 year: 2021 ident: bib15 article-title: Predicting cancer cells progression via entropy generation based on AR and ARMA models publication-title: Int Commun Heat Mass Tran – volume: 114 year: 2024 ident: bib27 article-title: Wind speed prediction and reconstruction based on improved grey wolf optimization algorithm and deep learning networks publication-title: Comput Electr Eng – volume: 222 year: 2023 ident: bib32 article-title: A novel hybrid model based on GA-VMD, sample entropy reconstruction and BiLSTM for wind speed prediction publication-title: Measurement – volume: 312 year: 2022 ident: bib10 article-title: A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting publication-title: Appl Energy – volume: 94 start-page: 629 year: 2016 end-page: 636 ident: bib29 article-title: Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method publication-title: Renew Energy – volume: 206 year: 2022 ident: bib35 article-title: Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM publication-title: Expert Syst Appl – volume: 254 year: 2022 ident: bib1 article-title: Variable frequency wind speed trend prediction system based on combined neural network and improved multi-objective optimization algorithm publication-title: Energy – volume: 239 start-page: 1226 year: 2019 end-page: 1241 ident: bib8 article-title: Scenario generation and probabilistic forecasting analysis of spatio-temporal wind speed series with multivariate autoregressive volatility models publication-title: Appl Energy – volume: 249 start-page: 392 year: 2022 end-page: 408 ident: bib2 article-title: Data processing strategies in wind energy forecasting models and applications: a comprehensive review publication-title: Appl Energy – volume: 156 start-page: 1373 year: 2020 ident: 10.1016/j.energy.2024.133510_bib19 article-title: Short-term wind speed prediction model based on GA-ANN improved by VMD publication-title: Renew Energy doi: 10.1016/j.renene.2019.12.047 – volume: 37 start-page: 3276 issue: 11 year: 2020 ident: 10.1016/j.energy.2024.133510_bib39 article-title: Crazy butterfly algorithm based on adaptive perturbation publication-title: Appl Res Comput – volume: 178 year: 2022 ident: 10.1016/j.energy.2024.133510_bib18 article-title: Comparative analysis of BPNN, SVR, LSTM, Random Forest, and LSTM-SVR for conditional simulation of non-Gaussian measured fluctuating wind pressures publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2022.109285 – volume: 162 start-page: 1208 year: 2020 ident: 10.1016/j.energy.2024.133510_bib17 article-title: Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm publication-title: Renew Energy doi: 10.1016/j.renene.2020.08.077 – volume: 47 start-page: 3669 issue: 3 year: 2022 ident: 10.1016/j.energy.2024.133510_bib30 article-title: Short-term wind power prediction based on data reconstruction and improved extreme learning machine publication-title: Arabian J Sci Eng doi: 10.1007/s13369-020-05311-x – volume: 79 start-page: 65 issue: 1 year: 2005 ident: 10.1016/j.energy.2024.133510_bib14 article-title: Forecast of hourly average wind speed with ARMA models in Navarre (Spain) publication-title: Sol Energy doi: 10.1016/j.solener.2004.09.013 – volume: 11 start-page: 2127 year: 2024 ident: 10.1016/j.energy.2024.133510_bib23 article-title: Ultra-short-term wind speed prediction based on TCN-MCM-EKF publication-title: Energy Rep doi: 10.1016/j.egyr.2024.01.058 – volume: 295 year: 2023 ident: 10.1016/j.energy.2024.133510_bib13 article-title: Deep-learning post-processing of short-term station precipitation based on NWP forecasts publication-title: Atmos Res doi: 10.1016/j.atmosres.2023.107032 – volume: 30 start-page: 89 issue: 1 year: 2008 ident: 10.1016/j.energy.2024.133510_bib46 article-title: Research on properties of combination forecasting model based on absolute of grey incidence publication-title: Syst Eng Electron – volume: 249 year: 2022 ident: 10.1016/j.energy.2024.133510_bib34 article-title: Wind speed estimation using novelty hybrid adaptive estimation model based on decomposition and deep learning methods (ICEEMDAN-CNN) publication-title: Energy doi: 10.1016/j.energy.2022.123785 – volume: 123 start-page: 694 year: 2018 ident: 10.1016/j.energy.2024.133510_bib36 article-title: An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm publication-title: Renew Energy doi: 10.1016/j.renene.2018.02.092 – volume: 187 start-page: 356 year: 2019 ident: 10.1016/j.energy.2024.133510_bib33 article-title: Multi-step short-term wind speed forecasting approach based on multi-scale dominant ingredient chaotic analysis, improved hybrid GWO-SCA optimization and ELM publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2019.02.086 – volume: 9 start-page: 1431 issue: 6 year: 2024 ident: 10.1016/j.energy.2024.133510_bib42 article-title: Machine-learning-based estimate of the wind speed over complex terrain using the long short-term memory (LSTM) recurrent neural network publication-title: Wind Energy Science doi: 10.5194/wes-9-1431-2024 – volume: 215 start-page: 643 year: 2018 ident: 10.1016/j.energy.2024.133510_bib31 article-title: A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting publication-title: Applied energy doi: 10.1016/j.apenergy.2018.02.070 – volume: 114 start-page: 75 year: 2016 ident: 10.1016/j.energy.2024.133510_bib3 article-title: Wind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithm publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2016.02.013 – volume: 28 issue: 9 year: 2021 ident: 10.1016/j.energy.2024.133510_bib16 article-title: Nonstationary wind speed data reconstruction based on secondary correction of statistical characteristics publication-title: Struct Control Health Monit doi: 10.1002/stc.2783 – volume: 238 year: 2024 ident: 10.1016/j.energy.2024.133510_bib22 article-title: Robust multi-step wind speed forecasting based on a graph-based data reconstruction deep learning method publication-title: Expert Syst Appl – volume: 204 start-page: 498 year: 2023 ident: 10.1016/j.energy.2024.133510_bib38 article-title: An opposition-based butterfly optimization algorithm with adaptive elite mutation in solving complex high-dimensional optimization problems publication-title: Math Comput Simulat doi: 10.1016/j.matcom.2022.08.020 – volume: 206 year: 2022 ident: 10.1016/j.energy.2024.133510_bib35 article-title: Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2022.117847 – volume: 127 year: 2021 ident: 10.1016/j.energy.2024.133510_bib15 article-title: Predicting cancer cells progression via entropy generation based on AR and ARMA models publication-title: Int Commun Heat Mass Tran – volume: 105 year: 2023 ident: 10.1016/j.energy.2024.133510_bib5 article-title: Condition monitoring of wind turbine based on deep learning networks and kernel principal component analysis publication-title: Comput Electr Eng doi: 10.1016/j.compeleceng.2022.108538 – volume: 146 start-page: 4057 issue: 12 year: 2018 ident: 10.1016/j.energy.2024.133510_bib11 article-title: An analog technique to improve storm wind speed prediction using a dual NWP model approach publication-title: Mon Weather Rev doi: 10.1175/MWR-D-17-0198.1 – start-page: 426 year: 2019 ident: 10.1016/j.energy.2024.133510_bib41 article-title: Deep learning based wind speed forecasting-A review – volume: 252 year: 2022 ident: 10.1016/j.energy.2024.133510_bib20 article-title: Integrated framework of extreme learning machine (ELM) based on improved atom search optimization for short-term wind speed prediction publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2021.115102 – volume: 94 start-page: 629 year: 2016 ident: 10.1016/j.energy.2024.133510_bib26 article-title: Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method publication-title: Renew Energy doi: 10.1016/j.renene.2016.03.103 – volume: 312 year: 2022 ident: 10.1016/j.energy.2024.133510_bib10 article-title: A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting publication-title: Appl Energy doi: 10.1016/j.apenergy.2022.118777 – volume: 9 start-page: 11 issue: 1 year: 2015 ident: 10.1016/j.energy.2024.133510_bib21 article-title: A power prediction method for photovoltaic power plant based on wavelet decomposition and artificial neural networks publication-title: Energies doi: 10.3390/en9010011 – volume: 254 year: 2022 ident: 10.1016/j.energy.2024.133510_bib1 article-title: Variable frequency wind speed trend prediction system based on combined neural network and improved multi-objective optimization algorithm publication-title: Energy doi: 10.1016/j.energy.2022.124249 – volume: 277 year: 2020 ident: 10.1016/j.energy.2024.133510_bib4 article-title: Hybrid system based on a multi-objective optimization and kernel approximation for multi-scale wind speed forecasting publication-title: Appl Energy doi: 10.1016/j.apenergy.2020.115561 – volume: 222 year: 2023 ident: 10.1016/j.energy.2024.133510_bib32 article-title: A novel hybrid model based on GA-VMD, sample entropy reconstruction and BiLSTM for wind speed prediction publication-title: Measurement doi: 10.1016/j.measurement.2023.113643 – volume: 239 start-page: 1226 year: 2019 ident: 10.1016/j.energy.2024.133510_bib8 article-title: Scenario generation and probabilistic forecasting analysis of spatio-temporal wind speed series with multivariate autoregressive volatility models publication-title: Appl Energy doi: 10.1016/j.apenergy.2019.02.015 – volume: 249 start-page: 392 year: 2022 ident: 10.1016/j.energy.2024.133510_bib2 article-title: Data processing strategies in wind energy forecasting models and applications: a comprehensive review publication-title: Appl Energy doi: 10.1016/j.apenergy.2019.04.188 – volume: 251 year: 2022 ident: 10.1016/j.energy.2024.133510_bib9 article-title: A novel offshore wind farm typhoon wind speed prediction model based on PSO–Bi-LSTM improved by VMD publication-title: Energy doi: 10.1016/j.energy.2022.123848 – volume: 9 start-page: 335 year: 2023 ident: 10.1016/j.energy.2024.133510_bib28 article-title: Short-term wind power forecasting based on SSA-VMD-LSTM publication-title: Energy Rep doi: 10.1016/j.egyr.2023.05.181 – volume: 28 start-page: 593 issue: 5 year: 2015 ident: 10.1016/j.energy.2024.133510_bib40 article-title: Optimal analog active filter design using craziness‐based particle swarm optimization algorithm publication-title: Int J Numer Model Electron Network Dev Field doi: 10.1002/jnm.2040 – volume: 15 start-page: 3055 issue: 9 year: 2022 ident: 10.1016/j.energy.2024.133510_bib24 article-title: Ultra-short-term wind power combined prediction based on complementary ensemble empirical mode decomposition, whale optimization algorithm, and Elman network publication-title: Energies doi: 10.3390/en15093055 – volume: 114 year: 2024 ident: 10.1016/j.energy.2024.133510_bib27 article-title: Wind speed prediction and reconstruction based on improved grey wolf optimization algorithm and deep learning networks publication-title: Comput Electr Eng doi: 10.1016/j.compeleceng.2024.109074 – volume: 250 year: 2022 ident: 10.1016/j.energy.2024.133510_bib43 article-title: Wind speed prediction based on multi-variable Capsnet-BILSTM-MOHHO for WPCCC publication-title: Energy doi: 10.1016/j.energy.2022.123761 – volume: 94 start-page: 629 year: 2016 ident: 10.1016/j.energy.2024.133510_bib29 article-title: Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method publication-title: Renew Energy doi: 10.1016/j.renene.2016.03.103 – volume: 278 year: 2023 ident: 10.1016/j.energy.2024.133510_bib44 article-title: Multi-head attention-based probabilistic CNN-BiLSTM for day-ahead wind speed forecasting publication-title: Energy doi: 10.1016/j.energy.2023.127865 – ident: 10.1016/j.energy.2024.133510_bib6 – volume: 282 year: 2023 ident: 10.1016/j.energy.2024.133510_bib12 article-title: Wind power ultra-short-term prediction method based on NWP wind speed correction and double clustering division of transitional weather process publication-title: Energy doi: 10.1016/j.energy.2023.128947 – volume: 157 start-page: 1 year: 2018 ident: 10.1016/j.energy.2024.133510_bib25 article-title: Short-term wind speed forecasting based on fast ensemble empirical mode decomposition, phase space reconstruction, sample entropy and improved back-propagation neural network publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2017.11.067 – volume: 197 start-page: 183 year: 2017 ident: 10.1016/j.energy.2024.133510_bib7 article-title: Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method publication-title: Applied energy doi: 10.1016/j.apenergy.2017.04.017 – volume: 20 start-page: 134 issue: 1 year: 2002 ident: 10.1016/j.energy.2024.133510_bib45 article-title: Comparing predictive accuracy publication-title: J Bus Econ Statistics doi: 10.1198/073500102753410444 – volume: 173 start-page: 123 year: 2018 ident: 10.1016/j.energy.2024.133510_bib37 article-title: A nonlinear hybrid wind speed forecasting model using LSTM network, hysteretic ELM and Differential Evolution algorithm publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2018.07.070 |
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| Title | A novel combined model based on advanced optimization algorithm, and deep learning model for abnormal wind speed identification and reconstruction |
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