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A multiobjective framework for wind speed prediction interval forecasts

Citations

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Cited by:

  1. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
  2. Yu, Guangzheng & Liu, Chengquan & Tang, Bo & Chen, Rusi & Lu, Liu & Cui, Chaoyue & Hu, Yue & Shen, Lingxu & Muyeen, S.M., 2022. "Short term wind power prediction for regional wind farms based on spatial-temporal characteristic distribution," Renewable Energy, Elsevier, vol. 199(C), pages 599-612.
  3. Theo, Wai Lip & Lim, Jeng Shiun & Ho, Wai Shin & Hashim, Haslenda & Lee, Chew Tin, 2017. "Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 531-573.
  4. Yang Hu & Yilin Qiao & Jingchun Chu & Ling Yuan & Lei Pan, 2019. "Joint Point-Interval Prediction and Optimization of Wind Power Considering the Sequential Uncertainties of Stepwise Procedure," Energies, MDPI, vol. 12(11), pages 1-21, June.
  5. Lu, Peng & Ye, Lin & Zhao, Yongning & Dai, Binhua & Pei, Ming & Tang, Yong, 2021. "Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges," Applied Energy, Elsevier, vol. 301(C).
  6. Lei Zhang & Lun Xie & Qinkai Han & Zhiliang Wang & Chen Huang, 2020. "Probability Density Forecasting of Wind Speed Based on Quantile Regression and Kernel Density Estimation," Energies, MDPI, vol. 13(22), pages 1-24, November.
  7. Mojtaba Qolipour & Ali Mostafaeipour & Mohammad Saidi-Mehrabad & Hamid R Arabnia, 2019. "Prediction of wind speed using a new Grey-extreme learning machine hybrid algorithm: A case study," Energy & Environment, , vol. 30(1), pages 44-62, February.
  8. Li, Ranran & Jin, Yu, 2018. "A wind speed interval prediction system based on multi-objective optimization for machine learning method," Applied Energy, Elsevier, vol. 228(C), pages 2207-2220.
  9. Latifa A. Yousef & Hibba Yousef & Lisandra Rocha-Meneses, 2023. "Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions," Energies, MDPI, vol. 16(24), pages 1-27, December.
  10. Mahmoud, Tawfek & Dong, Z.Y. & Ma, Jin, 2018. "An advanced approach for optimal wind power generation prediction intervals by using self-adaptive evolutionary extreme learning machine," Renewable Energy, Elsevier, vol. 126(C), pages 254-269.
  11. Lv, Sheng-Xiang & Wang, Lin, 2022. "Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization," Applied Energy, Elsevier, vol. 311(C).
  12. Liu, Tianhao & Shan, Linke & Jiang, Meihui & Li, Fangning & Kong, Fannie & Du, Pengcheng & Zhu, Hongyu & Goh, Hui Hwang & Kurniawan, Tonni Agustiono & Huang, Chao & Zhang, Dongdong, 2025. "Multi-dimensional data processing and intelligent forecasting technologies for renewable energy generation," Applied Energy, Elsevier, vol. 398(C).
  13. Nantian Huang & Chong Yuan & Guowei Cai & Enkai Xing, 2016. "Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine," Energies, MDPI, vol. 9(12), pages 1-19, November.
  14. Hugo Gaspar Hernandez-Palma & Jonny Rafael Plaza Alvarado & Jesús Enrique García Guiliany & Guilherme Luiz Dotto & Claudete Gindri Ramos, 2024. "Implications of Machine Learning in the Generation of Renewable Energies in Latin America from a Globalized Vision: A Systematic Review," International Journal of Energy Economics and Policy, Econjournals, vol. 14(2), pages 1-10, March.
  15. Hua Li & Zhen Wang & Binbin Shan & Lingling Li, 2022. "Research on Multi-Step Prediction of Short-Term Wind Power Based on Combination Model and Error Correction," Energies, MDPI, vol. 15(22), pages 1-21, November.
  16. Xiaomei Wu & Chun Sing Lai & Chenchen Bai & Loi Lei Lai & Qi Zhang & Bo Liu, 2020. "Optimal Kernel ELM and Variational Mode Decomposition for Probabilistic PV Power Prediction," Energies, MDPI, vol. 13(14), pages 1-21, July.
  17. Wang, H.Z. & Wang, G.B. & Li, G.Q. & Peng, J.C. & Liu, Y.T., 2016. "Deep belief network based deterministic and probabilistic wind speed forecasting approach," Applied Energy, Elsevier, vol. 182(C), pages 80-93.
  18. Liu, Hui & Mi, Xiwei & Li, Yanfei, 2018. "An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm," Renewable Energy, Elsevier, vol. 123(C), pages 694-705.
  19. Saeed, Adnan & Li, Chaoshun & Gan, Zhenhao, 2024. "Short-term wind speed interval prediction using improved quality-driven loss based gated multi-scale convolutional sequence model," Energy, Elsevier, vol. 300(C).
  20. Li, Chaoshun & Tang, Geng & Xue, Xiaoming & Chen, Xinbiao & Wang, Ruoheng & Zhang, Chu, 2020. "The short-term interval prediction of wind power using the deep learning model with gradient descend optimization," Renewable Energy, Elsevier, vol. 155(C), pages 197-211.
  21. Wang, Yun & Hu, Qinghua & Meng, Deyu & Zhu, Pengfei, 2017. "Deterministic and probabilistic wind power forecasting using a variational Bayesian-based adaptive robust multi-kernel regression model," Applied Energy, Elsevier, vol. 208(C), pages 1097-1112.
  22. Shi, Jinhao & Wang, Bo & Luo, Kaiyi & Wu, Yifei & Zhou, Min & Watada, Junzo, 2023. "Ultra-short-term wind power interval prediction based on multi-task learning and generative critic networks," Energy, Elsevier, vol. 272(C).
  23. Honghai Niu & Yu Yang & Lingchao Zeng & Yiguo Li, 2021. "ELM-QR-Based Nonparametric Probabilistic Prediction Method for Wind Power," Energies, MDPI, vol. 14(3), pages 1-15, January.
  24. Wang, Yun & Xu, Houhua & Zou, Runmin & Zhang, Lingjun & Zhang, Fan, 2022. "A deep asymmetric Laplace neural network for deterministic and probabilistic wind power forecasting," Renewable Energy, Elsevier, vol. 196(C), pages 497-517.
  25. Zhu, Lingwei & Xu, Bin & Wang, Xinrong & Yue, Hao & Mo, Ran & Wang, Sen & Zhao, Zenghai & Lu, Peng, 2025. "Stochastic simulation framework for renewable power output: Integrating hybrid discrete-continuous distributions with vine copula function," Renewable Energy, Elsevier, vol. 251(C).
  26. Zheng, Ling & Zhou, Bin & Or, Siu Wing & Cao, Yijia & Wang, Huaizhi & Li, Yong & Chan, Ka Wing, 2021. "Spatio-temporal wind speed prediction of multiple wind farms using capsule network," Renewable Energy, Elsevier, vol. 175(C), pages 718-730.
  27. Jianzhou Wang & Chunying Wu & Tong Niu, 2019. "A Novel System for Wind Speed Forecasting Based on Multi-Objective Optimization and Echo State Network," Sustainability, MDPI, vol. 11(2), pages 1-34, January.
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