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Short-term wind speed forecasting with Markov-switching model

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  1. D’Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2015. "Economic performance indicators of wind energy based on wind speed stochastic modeling," Applied Energy, Elsevier, vol. 154(C), pages 290-297.
  2. Zhao, Weigang & Wei, Yi-Ming & Su, Zhongyue, 2016. "One day ahead wind speed forecasting: A resampling-based approach," Applied Energy, Elsevier, vol. 178(C), pages 886-901.
  3. Li, Chaoshun & Xiao, Zhengguang & Xia, Xin & Zou, Wen & Zhang, Chu, 2018. "A hybrid model based on synchronous optimisation for multi-step short-term wind speed forecasting," Applied Energy, Elsevier, vol. 215(C), pages 131-144.
  4. Wang, Deyun & Luo, Hongyuan & Grunder, Olivier & Lin, Yanbing, 2017. "Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction," Renewable Energy, Elsevier, vol. 113(C), pages 1345-1358.
  5. 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.
  6. Tascikaraoglu, Akin & Sanandaji, Borhan M. & Poolla, Kameshwar & Varaiya, Pravin, 2016. "Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform," Applied Energy, Elsevier, vol. 165(C), pages 735-747.
  7. Xydas, Erotokritos & Qadrdan, Meysam & Marmaras, Charalampos & Cipcigan, Liana & Jenkins, Nick & Ameli, Hossein, 2017. "Probabilistic wind power forecasting and its application in the scheduling of gas-fired generators," Applied Energy, Elsevier, vol. 192(C), pages 382-394.
  8. Chinmoy, Lakshmi & Iniyan, S. & Goic, Ranko, 2019. "Modeling wind power investments, policies and social benefits for deregulated electricity market – A review," Applied Energy, Elsevier, vol. 242(C), pages 364-377.
  9. Verdejo, Humberto & Awerkin, Almendra & Saavedra, Eugenio & Kliemann, Wolfgang & Vargas, Luis, 2016. "Stochastic modeling to represent wind power generation and demand in electric power system based on real data," Applied Energy, Elsevier, vol. 173(C), pages 283-295.
  10. Duca, Victor E.L.A. & Fonseca, Thaís C.O. & Cyrino Oliveira, Fernando L., 2021. "A generalized dynamical model for wind speed forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
  11. 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.
  12. Rasmussen, Lisa Buth & Bacher, Peder & Madsen, Henrik & Nielsen, Henrik Aalborg & Heerup, Christian & Green, Torben, 2016. "Load forecasting of supermarket refrigeration," Applied Energy, Elsevier, vol. 163(C), pages 32-40.
  13. 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.
  14. Abdollahi, Hooman & Ebrahimi, Seyed Babak, 2020. "A new hybrid model for forecasting Brent crude oil price," Energy, Elsevier, vol. 200(C).
  15. repec:hum:wpaper:sfb649dp2015-026 is not listed on IDEAS
  16. Zhenhua Xiong & Yan Chen & Guihua Ban & Yixin Zhuo & Kui Huang, 2022. "A Hybrid Algorithm for Short-Term Wind Power Prediction," Energies, MDPI, vol. 15(19), pages 1-11, October.
  17. Ma, Long & Huang, Ling & Shi, Huifeng, 2023. "Multi-node wind speed forecasting based on a novel dynamic spatial–temporal graph network," Energy, Elsevier, vol. 285(C).
  18. Davoodi, Shayan & Fereydooni, Ali & Rastegar, Mohammad Ali, 2024. "Can portfolio construction considering ESG still gain high profits?," Research in International Business and Finance, Elsevier, vol. 67(PA).
  19. Wasilewski, J. & Baczynski, D., 2017. "Short-term electric energy production forecasting at wind power plants in pareto-optimality context," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 177-187.
  20. Pliego Marugán, Alberto & Peco Chacón, Ana María & García Márquez, Fausto Pedro, 2019. "Reliability analysis of detecting false alarms that employ neural networks: A real case study on wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
  21. Duca, Victor E.L.A. & Fonseca, Thais C.O. & Cyrino Oliveira, Fernando Luiz, 2022. "Joint modelling wind speed and power via Bayesian Dynamical models," Energy, Elsevier, vol. 247(C).
  22. Shen, Zhiwei & Ritter, Matthias, 2016. "Forecasting volatility of wind power production," Applied Energy, Elsevier, vol. 176(C), pages 295-308.
  23. Tahmasebifar, Reza & Moghaddam, Mohsen Parsa & Sheikh-El-Eslami, Mohammad Kazem & Kheirollahi, Reza, 2020. "A new hybrid model for point and probabilistic forecasting of wind power," Energy, Elsevier, vol. 211(C).
  24. Sun, Peng & Li, Jian & Wang, Caisheng & Lei, Xiao, 2016. "A generalized model for wind turbine anomaly identification based on SCADA data," Applied Energy, Elsevier, vol. 168(C), pages 550-567.
  25. Yuanzhuo Du & Kun Zhang & Qianzhi Shao & Zhe Chen, 2023. "A Short-Term Prediction Model of Wind Power with Outliers: An Integration of Long Short-Term Memory, Ensemble Empirical Mode Decomposition, and Sample Entropy," Sustainability, MDPI, vol. 15(7), pages 1-15, April.
  26. Ruiz de la Hermosa González-Carrato, Raúl, 2018. "Wind farm monitoring using Mahalanobis distance and fuzzy clustering," Renewable Energy, Elsevier, vol. 123(C), pages 526-540.
  27. Astolfi, Davide & Castellani, Francesco & Garinei, Alberto & Terzi, Ludovico, 2015. "Data mining techniques for performance analysis of onshore wind farms," Applied Energy, Elsevier, vol. 148(C), pages 220-233.
  28. Sun, Shaolong & Qiao, Han & Wei, Yunjie & Wang, Shouyang, 2017. "A new dynamic integrated approach for wind speed forecasting," Applied Energy, Elsevier, vol. 197(C), pages 151-162.
  29. Zhang, Yagang & Yang, Jingyun & Wang, Kangcheng & Wang, Zengping & Wang, Yinding, 2015. "Improved wind prediction based on the Lorenz system," Renewable Energy, Elsevier, vol. 81(C), pages 219-226.
  30. Yang, Xiuyuan & Xu, Minglu & Xu, Shouchen & Han, Xiaojuan, 2017. "Day-ahead forecasting of photovoltaic output power with similar cloud space fusion based on incomplete historical data mining," Applied Energy, Elsevier, vol. 206(C), pages 683-696.
  31. Lin, Qingcheng & Cai, Huiling & Liu, Hanwei & Li, Xuefeng & Xiao, Hui, 2024. "A novel ultra-short-term wind power prediction model jointly driven by multiple algorithm optimization and adaptive selection," Energy, Elsevier, vol. 288(C).
  32. Ban, Guihua & Chen, Yan & Xiong, Zhenhua & Zhuo, Yixin & Huang, Kui, 2024. "The univariate model for long-term wind speed forecasting based on wavelet soft threshold denoising and improved Autoformer," Energy, Elsevier, vol. 290(C).
  33. Kerolly Kedma Felix do Nascimento & Fábio Sandro dos Santos & Jader Silva Jale & Silvio Fernando Alves Xavier Júnior & Tiago A. E. Ferreira, 2023. "Extracting Rules via Markov Chains for Cryptocurrencies Returns Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 61(3), pages 1095-1114, March.
  34. Wang, Qin & Wu, Hongyu & Florita, Anthony R. & Brancucci Martinez-Anido, Carlo & Hodge, Bri-Mathias, 2016. "The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales," Applied Energy, Elsevier, vol. 184(C), pages 696-713.
  35. Jiang, Ping & Wang, Yun & Wang, Jianzhou, 2017. "Short-term wind speed forecasting using a hybrid model," Energy, Elsevier, vol. 119(C), pages 561-577.
  36. Feng, Cong & Cui, Mingjian & Hodge, Bri-Mathias & Zhang, Jie, 2017. "A data-driven multi-model methodology with deep feature selection for short-term wind forecasting," Applied Energy, Elsevier, vol. 190(C), pages 1245-1257.
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