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An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed

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  1. 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.
  2. Zhao, Jing & Guo, Zhenhai & Guo, Yanling & Lin, Wantao & Zhu, Wenjin, 2021. "A self-organizing forecast of day-ahead wind speed: Selective ensemble strategy based on numerical weather predictions," Energy, Elsevier, vol. 218(C).
  3. Mohammed A. A. Al-qaness & Ahmed A. Ewees & Mohamed Abd Elaziz & Ahmed H. Samak, 2022. "Wind Power Forecasting Using Optimized Dendritic Neural Model Based on Seagull Optimization Algorithm and Aquila Optimizer," Energies, MDPI, vol. 15(24), pages 1-14, December.
  4. 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.
  5. Rui Wang & Jingrui Li & Jianzhou Wang & Chengze Gao, 2018. "Research and Application of a Hybrid Wind Energy Forecasting System Based on Data Processing and an Optimized Extreme Learning Machine," Energies, MDPI, vol. 11(7), pages 1-29, July.
  6. Kazmi, Hussain & Tao, Zhenmin, 2022. "How good are TSO load and renewable generation forecasts: Learning curves, challenges, and the road ahead," Applied Energy, Elsevier, vol. 323(C).
  7. Sward, J.A. & Ault, T.R. & Zhang, K.M., 2023. "Spatial biases revealed by LiDAR in a multiphysics WRF ensemble designed for offshore wind," Energy, Elsevier, vol. 262(PA).
  8. Aziz Ezzat, Ahmed, 2020. "Turbine-specific short-term wind speed forecasting considering within-farm wind field dependencies and fluctuations," Applied Energy, Elsevier, vol. 269(C).
  9. Xiang Ying & Keke Zhao & Zhiqiang Liu & Jie Gao & Dongxiao He & Xuewei Li & Wei Xiong, 2022. "Wind Speed Prediction via Collaborative Filtering on Virtual Edge Expanding Graphs," Mathematics, MDPI, vol. 10(11), pages 1-16, June.
  10. Du, Pei & Wang, Jianzhou & Yang, Wendong & Niu, Tong, 2018. "Multi-step ahead forecasting in electrical power system using a hybrid forecasting system," Renewable Energy, Elsevier, vol. 122(C), pages 533-550.
  11. Sward, J.A. & Ault, T.R. & Zhang, K.M., 2022. "Genetic algorithm selection of the weather research and forecasting model physics to support wind and solar energy integration," Energy, Elsevier, vol. 254(PB).
  12. Jin, Yuqing & Ju, Ping & Rehtanz, Christian & Wu, Feng & Pan, Xueping, 2018. "Equivalent modeling of wind energy conversion considering overall effect of pitch angle controllers in wind farm," Applied Energy, Elsevier, vol. 222(C), pages 485-496.
  13. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
  14. Wang, Jian & Yang, Zhongshan, 2021. "Ultra-short-term wind speed forecasting using an optimized artificial intelligence algorithm," Renewable Energy, Elsevier, vol. 171(C), pages 1418-1435.
  15. Liu, Xingdou & Zhang, Li & Wang, Jiangong & Zhou, Yue & Gan, Wei, 2023. "A unified multi-step wind speed forecasting framework based on numerical weather prediction grids and wind farm monitoring data," Renewable Energy, Elsevier, vol. 211(C), pages 948-963.
  16. Yıldıran, Uğur & Kayahan, İsmail, 2018. "Risk-averse stochastic model predictive control-based real-time operation method for a wind energy generation system supported by a pumped hydro storage unit," Applied Energy, Elsevier, vol. 226(C), pages 631-643.
  17. Jin, Feng & Li, Yongwu & Sun, Shaolong & Li, Hongtao, 2020. "Forecasting air passenger demand with a new hybrid ensemble approach," Journal of Air Transport Management, Elsevier, vol. 83(C).
  18. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
  19. Liu, Guangbiao & Zhou, Jianzhong & Jia, Benjun & He, Feifei & Yang, Yuqi & Sun, Na, 2019. "Advance short-term wind energy quality assessment based on instantaneous standard deviation and variogram of wind speed by a hybrid method," Applied Energy, Elsevier, vol. 238(C), pages 643-667.
  20. Dong, Yingchao & Zhang, Hongli & Wang, Cong & Zhou, Xiaojun, 2021. "A novel hybrid model based on Bernstein polynomial with mixture of Gaussians for wind power forecasting," Applied Energy, Elsevier, vol. 286(C).
  21. Song, Jingjing & Wang, Jianzhou & Lu, Haiyan, 2018. "A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 215(C), pages 643-658.
  22. Liu, Chenyu & Zhang, Xuemin & Mei, Shengwei & Zhou, Qingyu & Fan, Hang, 2023. "Series-wise attention network for wind power forecasting considering temporal lag of numerical weather prediction," Applied Energy, Elsevier, vol. 336(C).
  23. Ping Jiang & Feng Liu & Yiliao Song, 2016. "A Hybrid Multi-Step Model for Forecasting Day-Ahead Electricity Price Based on Optimization, Fuzzy Logic and Model Selection," Energies, MDPI, vol. 9(8), pages 1-27, August.
  24. Yan, Jie & Möhrlen, Corinna & Göçmen, Tuhfe & Kelly, Mark & Wessel, Arne & Giebel, Gregor, 2022. "Uncovering wind power forecasting uncertainty sources and their propagation through the whole modelling chain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
  25. Jiang, Haiyan & Wang, Jianzhou & Wu, Jie & Geng, Wei, 2017. "Comparison of numerical methods and metaheuristic optimization algorithms for estimating parameters for wind energy potential assessment in low wind regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1199-1217.
  26. Yuan, Renyu & Ji, Wenju & Luo, Kun & Wang, Jianwen & Zhang, Sanxia & Wang, Qiang & Fan, Jianren & Ni, MingJiang & Cen, Kefa, 2017. "Coupled wind farm parameterization with a mesoscale model for simulations of an onshore wind farm," Applied Energy, Elsevier, vol. 206(C), pages 113-125.
  27. Shakeri, Mohammad & Shayestegan, Mohsen & Reza, S.M. Salim & Yahya, Iskandar & Bais, Badariah & Akhtaruzzaman, Md & Sopian, Kamaruzzaman & Amin, Nowshad, 2018. "Implementation of a novel home energy management system (HEMS) architecture with solar photovoltaic system as supplementary source," Renewable Energy, Elsevier, vol. 125(C), pages 108-120.
  28. Liu, Chenyu & Zhang, Xuemin & Mei, Shengwei & Zhen, Zhao & Jia, Mengshuo & Li, Zheng & Tang, Haiyan, 2022. "Numerical weather prediction enhanced wind power forecasting: Rank ensemble and probabilistic fluctuation awareness," Applied Energy, Elsevier, vol. 313(C).
  29. Wang, Jianzhou & Niu, Tong & Lu, Haiyan & Guo, Zhenhai & Yang, Wendong & Du, Pei, 2018. "An analysis-forecast system for uncertainty modeling of wind speed: A case study of large-scale wind farms," Applied Energy, Elsevier, vol. 211(C), pages 492-512.
  30. Yang, Zhongshan & Wang, Jian, 2018. "A hybrid forecasting approach applied in wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Energy, Elsevier, vol. 160(C), pages 87-100.
  31. Wang, Jianzhou & Dong, Yunxuan & Zhang, Kequan & Guo, Zhenhai, 2017. "A numerical model based on prior distribution fuzzy inference and neural networks," Renewable Energy, Elsevier, vol. 112(C), pages 486-497.
  32. Wu, Chunlei & Luo, Kun & Wang, Qiang & Fan, Jianren, 2022. "A refined wind farm parameterization for the weather research and forecasting model," Applied Energy, Elsevier, vol. 306(PB).
  33. Yang, Zhongshan & Wang, Jian, 2018. "A combination forecasting approach applied in multistep wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Applied Energy, Elsevier, vol. 230(C), pages 1108-1125.
  34. Jiří Jaromír Klemeš & Petar Sabev Varbanov & Paweł Ocłoń & Hon Huin Chin, 2019. "Towards Efficient and Clean Process Integration: Utilisation of Renewable Resources and Energy-Saving Technologies," Energies, MDPI, vol. 12(21), pages 1-32, October.
  35. Salvação, N. & Guedes Soares, C., 2018. "Wind resource assessment offshore the Atlantic Iberian coast with the WRF model," Energy, Elsevier, vol. 145(C), pages 276-287.
  36. Wang, Qiang & Luo, Kun & Yuan, Renyu & Zhang, Sanxia & Fan, Jianren, 2019. "Wake and performance interference between adjacent wind farms: Case study of Xinjiang in China by means of mesoscale simulations," Energy, Elsevier, vol. 166(C), pages 1168-1180.
  37. Han, Yan & Mi, Lihua & Shen, Lian & Cai, C.S. & Liu, Yuchen & Li, Kai & Xu, Guoji, 2022. "A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting," Applied Energy, Elsevier, vol. 312(C).
  38. Nadjemi, O. & Nacer, T. & Hamidat, A. & Salhi, H., 2017. "Optimal hybrid PV/wind energy system sizing: Application of cuckoo search algorithm for Algerian dairy farms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1352-1365.
  39. Wen, Songkang & Li, Yanting & Su, Yan, 2022. "A new hybrid model for power forecasting of a wind farm using spatial–temporal correlations," Renewable Energy, Elsevier, vol. 198(C), pages 155-168.
  40. Jialin Liu & Chen Gong & Suhua Chen & Nanrun Zhou, 2023. "Multi-Step-Ahead Wind Speed Forecast Method Based on Outlier Correction, Optimized Decomposition, and DLinear Model," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
  41. Zhang, Fei & Li, Peng-Cheng & Gao, Lu & Liu, Yong-Qian & Ren, Xiao-Ying, 2021. "Application of autoregressive dynamic adaptive (ARDA) model in real-time wind power forecasting," Renewable Energy, Elsevier, vol. 169(C), pages 129-143.
  42. Ping Jiang & Zeng Wang & Kequan Zhang & Wendong Yang, 2017. "An Innovative Hybrid Model Based on Data Pre-Processing and Modified Optimization Algorithm and Its Application in Wind Speed Forecasting," Energies, MDPI, vol. 10(7), pages 1-29, July.
  43. Li, Dan & Jiang, Fuxin & Chen, Min & Qian, Tao, 2022. "Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks," Energy, Elsevier, vol. 238(PC).
  44. Yuansheng Huang & Lei Yang & Shijian Liu & Guangli Wang, 2019. "Multi-Step Wind Speed Forecasting Based On Ensemble Empirical Mode Decomposition, Long Short Term Memory Network and Error Correction Strategy," Energies, MDPI, vol. 12(10), pages 1-22, May.
  45. Zhao, Jing & Guo, Yanling & Xiao, Xia & Wang, Jianzhou & Chi, Dezhong & Guo, Zhenhai, 2017. "Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method," Applied Energy, Elsevier, vol. 197(C), pages 183-202.
  46. Li, Qian & Loy-Benitez, Jorge & Nam, KiJeon & Hwangbo, Soonho & Rashidi, Jouan & Yoo, ChangKyoo, 2019. "Sustainable and reliable design of reverse osmosis desalination with hybrid renewable energy systems through supply chain forecasting using recurrent neural networks," Energy, Elsevier, vol. 178(C), pages 277-292.
  47. Yang, Wendong & Wang, Jianzhou & Niu, Tong & Du, Pei, 2019. "A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting," Applied Energy, Elsevier, vol. 235(C), pages 1205-1225.
  48. Dong, Qingli & Sun, Yuhuan & Li, Peizhi, 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, Elsevier, vol. 102(PA), pages 241-257.
  49. 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.
  50. Wang, Ying & Wang, Jianzhou & Li, Zhiwu & Yang, Hufang & Li, Hongmin, 2021. "Design of a combined system based on two-stage data preprocessing and multi-objective optimization for wind speed prediction," Energy, Elsevier, vol. 231(C).
  51. Wang, Han & Han, Shuang & Liu, Yongqian & Yan, Jie & Li, Li, 2019. "Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system," Applied Energy, Elsevier, vol. 237(C), pages 1-10.
  52. Wang, Huai-zhi & Li, Gang-qiang & Wang, Gui-bin & Peng, Jian-chun & Jiang, Hui & Liu, Yi-tao, 2017. "Deep learning based ensemble approach for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 188(C), pages 56-70.
  53. Yu, Ruiguo & Liu, Zhiqiang & Li, Xuewei & Lu, Wenhuan & Ma, Degang & Yu, Mei & Wang, Jianrong & Li, Bin, 2019. "Scene learning: Deep convolutional networks for wind power prediction by embedding turbines into grid space," Applied Energy, Elsevier, vol. 238(C), pages 249-257.
  54. 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.
  55. Xiaoyu Shi & Xuewen Lei & Qiang Huang & Shengzhi Huang & Kun Ren & Yuanyuan Hu, 2018. "Hourly Day-Ahead Wind Power Prediction Using the Hybrid Model of Variational Model Decomposition and Long Short-Term Memory," Energies, MDPI, vol. 11(11), pages 1-20, November.
  56. 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.
  57. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
  58. Yechi Zhang & Jianzhou Wang & Haiyan Lu, 2019. "Research and Application of a Novel Combined Model Based on Multiobjective Optimization for Multistep-Ahead Electric Load Forecasting," Energies, MDPI, vol. 12(10), pages 1-27, May.
  59. Jannik Schütz Roungkvist & Peter Enevoldsen, 2020. "Timescale classification in wind forecasting: A review of the state‐of‐the‐art," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 757-768, August.
  60. 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.
  61. Jiang, Ping & Wang, Biao & Li, Hongmin & Lu, Haiyan, 2019. "Modeling for chaotic time series based on linear and nonlinear framework: Application to wind speed forecasting," Energy, Elsevier, vol. 173(C), pages 468-482.
  62. Prósper, Miguel A. & Otero-Casal, Carlos & Fernández, Felipe Canoura & Miguez-Macho, Gonzalo, 2019. "Wind power forecasting for a real onshore wind farm on complex terrain using WRF high resolution simulations," Renewable Energy, Elsevier, vol. 135(C), pages 674-686.
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  67. Pei Du & Yu Jin & Kequan Zhang, 2016. "A Hybrid Multi-Step Rolling Forecasting Model Based on SSA and Simulated Annealing—Adaptive Particle Swarm Optimization for Wind Speed," Sustainability, MDPI, vol. 8(8), pages 1-25, August.
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