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Research Article

Combined wind speed forecasting model based on secondary decomposition and quantile regression closed-form continuous-time neural network

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Pages 1793-1814 | Received 27 Jul 2023, Accepted 06 Oct 2023, Published online: 23 Oct 2023

References

  • Bai, Y., M. D. Liu, L. Ding, and Y. J. Ma. 2021. Double-layer staged training echo-state networks for wind speed prediction using variational mode decomposition. Applied Energy 301:117461. doi:10.1016/j.apenergy.2021.117461.
  • Bates, J. M., and C. W. J. Granger. 1969. The combination of forecasts. Journal of the Operational Research Society 20 (4):451–68. doi:10.1057/jors.1969.103.
  • Bo, H., X. Niu, and J. Wang. 2019. Wind speed forecasting system based on the variational mode decomposition strategy and immune selection multi-objective dragonfly optimization algorithm. IEEE Access 7:178063–81. doi:10.1109/ACCESS.2019.2957062.
  • da Silva, R. G., S. R. Moreno, M. H. D. M. Ribeiro, J. H. K. Larcher, V. C. Mariani, and L. dos Santos Coelho. 2022. Multi-step short-term wind speed forecasting based on multi-stage decomposition coupled with stacking-ensemble learning approach. International Journal of Electrical Power & Energy Systems 143:108504. doi:10.1016/j.ijepes.2022.108504.
  • Dong, Q., Y. Sun, and P. Li. 2017. A novel forecasting model based on a hybrid processing strategy and an optimized local linear fuzzy neural network to make wind power forecasting: A case study of wind farms in China. Renewable Energy 102:241–57. doi:10.1016/j.renene.2016.10.030.
  • Duong, T., and M. L. Hazelton. 2005. Cross-validation bandwidth matrices for multivariate kernel density estimation. Scandinavian Journal of Statistics 32 (3):485–506. doi:10.1111/j.1467-9469.2005.00445.x.
  • Golyandina, N., V. Nekrutkin, and A. Zhigljavsky. 2001. Analysis of time series structure: SSA and related techniques. CRC press. https://www.gistatgroup.com/cat/book2/index.html.
  • Hasani, R., M. Lechner, A. Amini, L. Liebenwein, A. Ray, M. Tschaikowski, G. Teschl, and D. Rus. 2022. Closed-form continuous-time neural networks. Nature Machine Intelligence 4 (11):992–1003. doi:10.1038/s42256-022-00556-7.
  • He, Z., Y. Chen, Z. Shang, C. Li, L. Li, and M. Xu. 2019. A novel wind speed forecasting model based on moving window and multi-objective particle swarm optimization algorithm. Applied Mathematical Modelling 76:717–40. doi:10.1016/j.apm.2019.07.001.
  • Hu, H., L. Wang, D. Zhang, and L. Ling. 2023. Rolling decomposition method in fusion with echo state network for wind speed forecasting. Renewable Energy 216:119101. doi:10.1016/j.renene.2023.119101.
  • Jaseena, K. U., and B. C. Kovoor. 2021. Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks. Energy Conversion and Management 234:113944. doi:10.1016/j.enconman.2021.113944.
  • Ji, L., C. Fu, Z. Ju, Y. Shi, S. Wu, and L. Tao. 2022. Short-term canyon wind speed prediction based on CNN—GRU transfer learning. Atmosphere 13 (5):813. doi:10.3390/atmos13050813.
  • Khosravi, A., S. Nahavandi, D. Creighton, and A. Atiya. 2011. Comprehensive review of neural network-based prediction intervals and new advances. IEEE Transactions on Neural Networks 22 (9):1341–56. doi:10.1109/TNN.2011.2162110.
  • Koenker, R., and K. F. Hallock. 2001. Quantile regression. Journal of Economic Perspectives 15 (4):143–56. doi:10.1257/jep.15.4.143.
  • Konstantin, D., and D. Zosso. 2013. Variational mode decomposition. IEEE Transactions on Signal Processing 62 (3):531–44. doi:10.1109/TSP.2013.2288675.
  • Liang, T., G. Xie, S. FAN, and Z. Meng. 2020a. A combined model based on CEEMDAN, permutation entropy, gated recurrent unit network, and an improved bat algorithm for wind speed forecasting. IEEE Access 8:165612–30. doi:10.1109/ACCESS.2020.3022872.
  • Liang, T., G. Xie, S. Fan, and Z. Meng. 2020b. A combined model based on ceemdan, permutation entropy, gated recurrent unit network, and an improved bat algorithm for wind speed forecasting. IEEE Access 8:165612–30. doi:10.1109/ACCESS.2020.3022872.
  • Li, D., F. Jiang, M. Chen, and T. Qian. 2022. Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks. Energy 238:121981. doi:10.1016/j.energy.2021.121981.
  • Li, R., and Y. Jin. 2018. A wind speed interval prediction system based on multi-objective optimization for machine learning method. Applied Energy 228:2207–20. doi:10.1016/j.apenergy.2018.07.032.
  • Liu, H., and C. Chen. 2019. Multi-objective data-ensemble wind speed forecasting model with stacked sparse autoencoder and adaptive decomposition-based error correction. Applied Energy 254:113686. doi:10.1016/j.apenergy.2019.113686.
  • Liu, H., H. Q. Tian, X. F. Liang, and Y. F. Li. 2015. Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks. Applied Energy 157:183–94. doi:10.1016/j.apenergy.2015.08.014.
  • Liu, Y., L. Ye, H. Qin, S. Ouyang, Z. Zhang, and J. Zhou. 2019. Middle and long-term runoff probabilistic forecasting based on gaussian mixture regression. Water Resources Management 33 (5):1785–99. doi:10.1007/s11269-019-02221-y.
  • Mi, X., H. Liu, and Y. Li. 2019. Wind speed prediction model using singular spectrum analysis, empirical mode decomposition and convolutional support vector machine. Energy Conversion and Management 180:196–205. doi:10.1016/j.enconman.2018.11.006.
  • Mi, X. W., H. Liu, and Y. F. Li. 2017. Wind speed forecasting method using wavelet, extreme learning machine and outlier correction algorithm. Energy Conversion and Management 151:709–22. doi:10.1016/j.enconman.2017.09.034.
  • Mirjalili, S. 2015. The ant lion optimizer. Advances in Engineering Software 83:80–98. doi:10.1016/j.advengsoft.2015.01.010.
  • Mirjalili, S., P. Jangir, and S. Saremi. 2017. Multi-objective ant lion optimizer: A multi-objective optimization algorithm for solving engineering problems. Applied Intelligence 46 (1):79–95. doi:10.1007/s10489-016-0825-8.
  • Moreno, S. R., R. G. da Silva, V. C. Mariani, and L. dos Santos Coelho. 2020. Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network. Energy Conversion and Management 213:112869. doi:10.1016/j.enconman.2020.112869.
  • Neshat, M., M. M. Nezhad, S. Mirjalili, G. Piras, and D. A. Garcia. 2022. Quaternion convolutional long short-term memory neural model with an adaptive decomposition method for wind speed forecasting: North Aegean islands case studies. Energy Conversion and Management 259:115590. doi:10.1016/j.enconman.2022.115590.
  • Ribeiro, M. D. M., S. Moreno, R. G. Silva, J. H. K. Larcher, C. Canton; V. Mariani, L. Coelho. 2022. Wind power forecasting based on bagging extreme learning machine ensemble model[C]. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Brugge, Belgium 5–7.
  • Richman, J. S., and J. R. Moorman. 2000. Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology Heart and Circulatory Physiology 278 (6):H2039–H49. doi:10.1152/ajpheart.2000.278.6.H2039.
  • Sun, N., J. Zhou, L. Chen, B. Jia, M. Tayyab, and T. Peng. 2018. An adaptive dynamic short-term wind speed forecasting model using secondary decomposition and an improved regularized extreme learning machine. Energy 165:939–57. doi:10.1016/j.energy.2018.09.180.
  • Taylor, J. W. 2000. A quantile regression neural network approach to estimating the conditional density of multiperiod returns. Journal of Forecasting 19 (4):299–311. doi:10.1002/1099-131X(200007)19:4<299:AID-FOR775>3.0.CO;2-V.
  • Wang, H. Z., G. B. Wang, G. Q. Li, J. C. Peng, and Y. T. Liu. 2016. Deep belief network based deterministic and probabilistic wind speed forecasting approach. Applied Energy 182:80–93. doi:10.1016/j.apenergy.2016.08.108.
  • Wu, C. Y., J. Z. Wang, X. J. Chen, P. Du, and W. D. Yang. 2020. A novel hybrid system based on multi-objective optimization for wind speed forecasting. Renewable Energy 146:149–65. doi:10.1016/j.renene.2019.04.157.
  • Xiang, L., J. Li, A. Hu, and Y. Zhang. 2020. Deterministic and probabilistic multi-step forecasting for short-term wind speed based on secondary decomposition and a deep learning method. Energy Conversion and Management 220:113098. doi:10.1016/j.enconman.2020.113098.
  • Xiao, L., Y. X. Dong, and Y. Dong. 2018. An improved combination approach based on adaboost algorithm for wind speed time series forecasting. Energy Conversion and Management 160:273–88. doi:10.1016/j.enconman.2018.01.038.
  • Xiao, L., J. Z. Wang, Y. Dong, and J. Wu. 2015. Combined forecasting models for wind energy forecasting: A case study in China. Renewable and Sustainable Energy Reviews 44:271–88. doi:10.1016/j.rser.2014.12.012.
  • Yang, M., S. Zhu, X. Han, and H. Wang. 2013. Joint probability density forecast for wind farm output in multi-time-interval. Dianli Xitong Zidonghua (automation of Electric power Systems. 37 (10):23–28. doi:10.7500/AEPS201205097.
  • Yu, L., Z. Wang, and L. Tang. 2015. A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting. Applied Energy 156:251–67. doi:10.1016/j.apenergy.2015.07.025.
  • Zhang, Y. G., and G. F. Pan. 2020. A hybrid prediction model for forecasting wind energy resources. Environmental Science and Pollution Research 27 (16):19428–46. doi:10.1007/s11356-020-08452-6.
  • Zhang, D., X. Peng, K. Pan, and Y. Liu. 2019. A novel wind speed forecasting based on hybrid decomposition and online sequential outlier robust extreme learning machine. Energy Conversion and Management 180:338–57. doi:10.1016/j.enconman.2018.10.089.
  • Zhang, Z., H. Qin, Y. Liu, L. Yao, X. Yu, J. Lu, Z. Jiang, and Z. Feng. 2019. Wind speed forecasting based on quantile regression minimal gated memory network and kernel density estimation. Energy Conversion and Management 196:1395–409. doi:10.1016/j.enconman.2019.06.024.
  • Zhang, G., Y. Wu, and Y. Liu. 2014. An advanced wind speed multi-step ahead forecasting approach with characteristic component analysis. Journal of Renewable and Sustainable Energy 6 (5). doi: 10.1063/1.4900556.
  • Zhu, X., R. Liu, Y. Chen, X. Gao, Y. Wang, and Z. Xu. 2021. Wind speed behaviors feather analysis and its utilization on wind speed prediction using 3D-CNN. Energy 236:121523. doi:10.1016/j.energy.2021.121523.

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