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Short-term wind speed forecasting based on spectral clustering and optimised echo state networks

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  1. Suárez-Cetrulo, Andrés L. & Burnham-King, Lauren & Haughton, David & Carbajo, Ricardo Simón, 2022. "Wind power forecasting using ensemble learning for day-ahead energy trading," Renewable Energy, Elsevier, vol. 191(C), pages 685-698.
  2. Wei Sun & Mohan Liu & Yi Liang, 2015. "Wind Speed Forecasting Based on FEEMD and LSSVM Optimized by the Bat Algorithm," Energies, MDPI, vol. 8(7), pages 1-23, June.
  3. Xiaomin Xu & Dongxiao Niu & Ming Fu & Huicong Xia & Han Wu, 2015. "A Multi Time Scale Wind Power Forecasting Model of a Chaotic Echo State Network Based on a Hybrid Algorithm of Particle Swarm Optimization and Tabu Search," Energies, MDPI, vol. 8(11), pages 1-21, November.
  4. 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).
  5. 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.
  6. Dongxiao Niu & Haichao Wang & Hanyu Chen & Yi Liang, 2017. "The General Regression Neural Network Based on the Fruit Fly Optimization Algorithm and the Data Inconsistency Rate for Transmission Line Icing Prediction," Energies, MDPI, vol. 10(12), pages 1-20, December.
  7. Nantian Huang & Enkai Xing & Guowei Cai & Zhiyong Yu & Bin Qi & Lin Lin, 2018. "Short-Term Wind Speed Forecasting Based on Low Redundancy Feature Selection," Energies, MDPI, vol. 11(7), pages 1-19, June.
  8. 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.
  9. Csereklyei, Zsuzsanna & Thurner, Paul W. & Langer, Johannes & Küchenhoff, Helmut, 2017. "Energy paths in the European Union: A model-based clustering approach," Energy Economics, Elsevier, vol. 65(C), pages 442-457.
  10. Dongxiao Niu & Yi Liang & Wei-Chiang Hong, 2017. "Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA," Energies, MDPI, vol. 10(12), pages 1-18, December.
  11. Xin Wu & Hong Wang & Guoqian Jiang & Ping Xie & Xiaoli Li, 2019. "Monitoring Wind Turbine Gearbox with Echo State Network Modeling and Dynamic Threshold Using SCADA Vibration Data," Energies, MDPI, vol. 12(6), pages 1-19, March.
  12. Da Liu & Kun Sun & Han Huang & Pingzhou Tang, 2018. "Monthly Load Forecasting Based on Economic Data by Decomposition Integration Theory," Sustainability, MDPI, vol. 10(9), pages 1-22, September.
  13. Liu, Hui & Chen, Chao, 2019. "Data processing strategies in wind energy forecasting models and applications: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 392-408.
  14. Hui Wang & Jianbo Sun & Weijun Wang, 2018. "Photovoltaic Power Forecasting Based on EEMD and a Variable-Weight Combination Forecasting Model," Sustainability, MDPI, vol. 10(8), pages 1-11, July.
  15. Tan, Jing & Liu, Hui & Li, Yanfei & Yin, Shi & Yu, Chengqing, 2022. "A new ensemble spatio-temporal PM2.5 prediction method based on graph attention recursive networks and reinforcement learning," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
  16. Yuewei Liu & Shenghui Zhang & Xuejun Chen & Jianzhou Wang, 2018. "Artificial Combined Model Based on Hybrid Nonlinear Neural Network Models and Statistics Linear Models—Research and Application for Wind Speed Forecasting," Sustainability, MDPI, vol. 10(12), pages 1-30, December.
  17. Lu, Peng & Ye, Lin & Zhao, Yongning & Dai, Binhua & Pei, Ming & Li, Zhuo, 2021. "Feature extraction of meteorological factors for wind power prediction based on variable weight combined method," Renewable Energy, Elsevier, vol. 179(C), pages 1925-1939.
  18. Li, Pei-Hao & Pye, Steve & Keppo, Ilkka, 2020. "Using clustering algorithms to characterise uncertain long-term decarbonisation pathways," Applied Energy, Elsevier, vol. 268(C).
  19. 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.
  20. Hui Wang & Jingxuan Sun & Jianbo Sun & Jilong Wang, 2017. "Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models," Energies, MDPI, vol. 10(10), pages 1-13, October.
  21. Niu, Xinsong & Wang, Jiyang, 2019. "A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 241(C), pages 519-539.
  22. Qiang Zhao & Kunkun Bao & Jia Wang & Yinghua Han & Jinkuan Wang, 2019. "An Online Hybrid Model for Temperature Prediction of Wind Turbine Gearbox Components," Energies, MDPI, vol. 12(20), pages 1-20, October.
  23. 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.
  24. Yimei Wang & Yongqian Liu & Li Li & David Infield & Shuang Han, 2018. "Short-Term Wind Power Forecasting Based on Clustering Pre-Calculated CFD Method," Energies, MDPI, vol. 11(4), pages 1-19, April.
  25. Wang, Lin & Lv, Sheng-Xiang & Zeng, Yu-Rong, 2018. "Effective sparse adaboost method with ESN and FOA for industrial electricity consumption forecasting in China," Energy, Elsevier, vol. 155(C), pages 1013-1031.
  26. Chen, Xi & Yu, Ruyi & Ullah, Sajid & Wu, Dianming & Li, Zhiqiang & Li, Qingli & Qi, Honggang & Liu, Jihui & Liu, Min & Zhang, Yundong, 2022. "A novel loss function of deep learning in wind speed forecasting," Energy, Elsevier, vol. 238(PB).
  27. Stefenon, Stefano Frizzo & Seman, Laio Oriel & Aquino, Luiza Scapinello & Coelho, Leandro dos Santos, 2023. "Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants," Energy, Elsevier, vol. 274(C).
  28. Qian, Zheng & Pei, Yan & Zareipour, Hamidreza & Chen, Niya, 2019. "A review and discussion of decomposition-based hybrid models for wind energy forecasting applications," Applied Energy, Elsevier, vol. 235(C), pages 939-953.
  29. He, Qingqing & Wang, Jianzhou & Lu, Haiyan, 2018. "A hybrid system for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 226(C), pages 756-771.
  30. Kim, Deockho & Hur, Jin, 2018. "Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method," Energy, Elsevier, vol. 157(C), pages 211-226.
  31. Chen, Xue-Jun & Zhao, Jing & Jia, Xiao-Zhong & Li, Zhong-Long, 2021. "Multi-step wind speed forecast based on sample clustering and an optimized hybrid system," Renewable Energy, Elsevier, vol. 165(P1), pages 595-611.
  32. Chidean, Mihaela I. & Caamaño, Antonio J. & Ramiro-Bargueño, Julio & Casanova-Mateo, Carlos & Salcedo-Sanz, Sancho, 2018. "Spatio-temporal analysis of wind resource in the Iberian Peninsula with data-coupled clustering," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2684-2694.
  33. Li, Chen & Zhu, Zhijie & Yang, Hufang & Li, Ranran, 2019. "An innovative hybrid system for wind speed forecasting based on fuzzy preprocessing scheme and multi-objective optimization," Energy, Elsevier, vol. 174(C), pages 1219-1237.
  34. 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.
  35. Qi Ouyang & Wenxi Lu, 2018. "Monthly Rainfall Forecasting Using Echo State Networks Coupled with Data Preprocessing Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(2), pages 659-674, January.
  36. Zhilong Wang & Chen Wang & Jie Wu, 2016. "Wind Energy Potential Assessment and Forecasting Research Based on the Data Pre-Processing Technique and Swarm Intelligent Optimization Algorithms," Sustainability, MDPI, vol. 8(11), pages 1-32, November.
  37. Liu, Hui & Yu, Chengqing & Wu, Haiping & Duan, Zhu & Yan, Guangxi, 2020. "A new hybrid ensemble deep reinforcement learning model for wind speed short term forecasting," Energy, Elsevier, vol. 202(C).
  38. 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.
  39. Arezoo Ghazanfari, 2022. "What Drives Petrol Price Dispersion across Australian Cities?," Energies, MDPI, vol. 15(16), pages 1-24, August.
  40. 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.
  41. Erick López & Carlos Valle & Héctor Allende & Esteban Gil & Henrik Madsen, 2018. "Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory," Energies, MDPI, vol. 11(3), pages 1-22, February.
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