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Support vector machines for wind speed prediction

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Cited by:

  1. Dhiman, Harsh S. & Deb, Dipankar & Guerrero, Josep M., 2019. "Hybrid machine intelligent SVR variants for wind forecasting and ramp events," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 369-379.
  2. Tang, Zhenhao & Zhao, Gengnan & Ouyang, Tinghui, 2021. "Two-phase deep learning model for short-term wind direction forecasting," Renewable Energy, Elsevier, vol. 173(C), pages 1005-1016.
  3. Wang, Jianzhou & Xiong, Shenghua, 2014. "A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China," Energy, Elsevier, vol. 76(C), pages 526-541.
  4. Zhang, Chi & Wei, Haikun & Zhao, Junsheng & Liu, Tianhong & Zhu, Tingting & Zhang, Kanjian, 2016. "Short-term wind speed forecasting using empirical mode decomposition and feature selection," Renewable Energy, Elsevier, vol. 96(PA), pages 727-737.
  5. Cai, Haoshu & Jia, Xiaodong & Feng, Jianshe & Yang, Qibo & Hsu, Yuan-Ming & Chen, Yudi & Lee, Jay, 2019. "A combined filtering strategy for short term and long term wind speed prediction with improved accuracy," Renewable Energy, Elsevier, vol. 136(C), pages 1082-1090.
  6. Liu, Hui & Chen, Chao & Tian, Hong-qi & Li, Yan-fei, 2012. "A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks," Renewable Energy, Elsevier, vol. 48(C), pages 545-556.
  7. Wang, Jianzhou & Qin, Shanshan & Zhou, Qingping & Jiang, Haiyan, 2015. "Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China," Renewable Energy, Elsevier, vol. 76(C), pages 91-101.
  8. Salcedo-Sanz, S. & Pastor-Sánchez, A. & Del Ser, J. & Prieto, L. & Geem, Z.W., 2015. "A Coral Reefs Optimization algorithm with Harmony Search operators for accurate wind speed prediction," Renewable Energy, Elsevier, vol. 75(C), pages 93-101.
  9. Bilgili, Mehmet & Sahin, Besir & Yasar, Abdulkadir, 2007. "Application of artificial neural networks for the wind speed prediction of target station using reference stations data," Renewable Energy, Elsevier, vol. 32(14), pages 2350-2360.
  10. A. Alexandridis & A. Zapranis, 2013. "Wind Derivatives: Modeling and Pricing," Computational Economics, Springer;Society for Computational Economics, vol. 41(3), pages 299-326, March.
  11. Park, BeomJun & Hur, Jin, 2018. "Spatial prediction of renewable energy resources for reinforcing and expanding power grids," Energy, Elsevier, vol. 164(C), pages 757-772.
  12. Shrivastava, Nitin Anand & Lohia, Kunal & Panigrahi, Bijaya Ketan, 2016. "A multiobjective framework for wind speed prediction interval forecasts," Renewable Energy, Elsevier, vol. 87(P2), pages 903-910.
  13. Sharifzadeh, Mahdi & Sikinioti-Lock, Alexandra & Shah, Nilay, 2019. "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 513-538.
  14. Jiani Heng & Chen Wang & Xuejing Zhao & Liye Xiao, 2016. "Research and Application Based on Adaptive Boosting Strategy and Modified CGFPA Algorithm: A Case Study for Wind Speed Forecasting," Sustainability, MDPI, vol. 8(3), pages 1-25, March.
  15. Hu, Jianming & Wang, Jianzhou & Zeng, Guowei, 2013. "A hybrid forecasting approach applied to wind speed time series," Renewable Energy, Elsevier, vol. 60(C), pages 185-194.
  16. Lahouar, A. & Ben Hadj Slama, J., 2017. "Hour-ahead wind power forecast based on random forests," Renewable Energy, Elsevier, vol. 109(C), pages 529-541.
  17. Xu, Jiuping & Liu, Tingting, 2020. "Technological paradigm-based approaches towards challenges and policy shifts for sustainable wind energy development," Energy Policy, Elsevier, vol. 142(C).
  18. Khosravi, A. & Machado, L. & Nunes, R.O., 2018. "Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil," Applied Energy, Elsevier, vol. 224(C), pages 550-566.
  19. Navas, R Kaja Bantha & Prakash, S & Sasipraba, T, 2020. "Artificial Neural Network based computing model for wind speed prediction: A case study of Coimbatore, Tamil Nadu, India," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
  20. Rehman, Shafiqur & Al-Abbadi, Naif M., 2007. "Wind shear coefficients and energy yield for Dhahran, Saudi Arabia," Renewable Energy, Elsevier, vol. 32(5), pages 738-749.
  21. Lei, Ma & Shiyan, Luan & Chuanwen, Jiang & Hongling, Liu & Yan, Zhang, 2009. "A review on the forecasting of wind speed and generated power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(4), pages 915-920, May.
  22. Salcedo-Sanz, Sancho & Ángel M. Pérez-Bellido, & Ortiz-García, Emilio G. & Portilla-Figueras, Antonio & Prieto, Luis & Paredes, Daniel, 2009. "Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction," Renewable Energy, Elsevier, vol. 34(6), pages 1451-1457.
  23. Rehman, S. & El-Amin, I.M. & Ahmad, F. & Shaahid, S.M. & Al-Shehri, A.M. & Bakhashwain, J.M. & Shash, A., 2007. "Feasibility study of hybrid retrofits to an isolated off-grid diesel power plant," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(4), pages 635-653, May.
  24. Qian Zhang & Kin Keung Lai & Dongxiao Niu & Qiang Wang & Xuebin Zhang, 2012. "A Fuzzy Group Forecasting Model Based on Least Squares Support Vector Machine (LS-SVM) for Short-Term Wind Power," Energies, MDPI, vol. 5(9), pages 1-18, September.
  25. Ana Lagos & Joaquín E. Caicedo & Gustavo Coria & Andrés Romero Quete & Maximiliano Martínez & Gastón Suvire & Jesús Riquelme, 2022. "State-of-the-Art Using Bibliometric Analysis of Wind-Speed and -Power Forecasting Methods Applied in Power Systems," Energies, MDPI, vol. 15(18), pages 1-40, September.
  26. Xiao Liu & Xu Lai & Jin Zou, 2017. "A New MCP Method of Wind Speed Temporal Interpolation and Extrapolation Considering Wind Speed Mixed Uncertainty," Energies, MDPI, vol. 10(8), pages 1-21, August.
  27. Vijendra Kumar & Hazi Md. Azamathulla & Kul Vaibhav Sharma & Darshan J. Mehta & Kiran Tota Maharaj, 2023. "The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management," Sustainability, MDPI, vol. 15(13), pages 1-33, July.
  28. Zhao, Pan & Wang, Jiangfeng & Xia, Junrong & Dai, Yiping & Sheng, Yingxin & Yue, Jie, 2012. "Performance evaluation and accuracy enhancement of a day-ahead wind power forecasting system in China," Renewable Energy, Elsevier, vol. 43(C), pages 234-241.
  29. Rodrigues, Eugénio & Gomes, Álvaro & Gaspar, Adélio Rodrigues & Henggeler Antunes, Carlos, 2018. "Estimation of renewable energy and built environment-related variables using neural networks – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 959-988.
  30. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
  31. Wu, Jie & Li, Na & Zhao, Yan & Wang, Jujie, 2022. "Usage of correlation analysis and hypothesis test in optimizing the gated recurrent unit network for wind speed forecasting," Energy, Elsevier, vol. 242(C).
  32. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
  33. Syed Muhammad Mohsin & Tahir Maqsood & Sajjad Ahmed Madani, 2022. "Solar and Wind Energy Forecasting for Green and Intelligent Migration of Traditional Energy Sources," Sustainability, MDPI, vol. 14(23), pages 1-20, December.
  34. Guo, Zhenhai & Zhao, Weigang & Lu, Haiyan & Wang, Jianzhou, 2012. "Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model," Renewable Energy, Elsevier, vol. 37(1), pages 241-249.
  35. Zeng, Jianwu & Qiao, Wei, 2013. "Short-term solar power prediction using a support vector machine," Renewable Energy, Elsevier, vol. 52(C), pages 118-127.
  36. Hong, Wei-Chiang, 2011. "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm," Energy, Elsevier, vol. 36(9), pages 5568-5578.
  37. Zhang, Lifang & Wang, Jianzhou & Niu, Xinsong & Liu, Zhenkun, 2021. "Ensemble wind speed forecasting with multi-objective Archimedes optimization algorithm and sub-model selection," Applied Energy, Elsevier, vol. 301(C).
  38. 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.
  39. Wang, Jian-Zhou & Wang, Yun & Jiang, Ping, 2015. "The study and application of a novel hybrid forecasting model – A case study of wind speed forecasting in China," Applied Energy, Elsevier, vol. 143(C), pages 472-488.
  40. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
  41. Hong, Wei-Chiang, 2010. "Application of chaotic ant swarm optimization in electric load forecasting," Energy Policy, Elsevier, vol. 38(10), pages 5830-5839, October.
  42. Jinliang Zhang & YiMing Wei & Zhong-fu Tan & Wang Ke & Wei Tian, 2017. "A Hybrid Method for Short-Term Wind Speed Forecasting," Sustainability, MDPI, vol. 9(4), pages 1-10, April.
  43. Neeraj Bokde & Andrés Feijóo & Daniel Villanueva & Kishore Kulat, 2018. "A Novel and Alternative Approach for Direct and Indirect Wind-Power Prediction Methods," Energies, MDPI, vol. 11(11), pages 1-19, October.
  44. Charakopoulos, Avraam & Karakasidis, Theodoros & Sarris, loannis, 2019. "Pattern identification for wind power forecasting via complex network and recurrence plot time series analysis," Energy Policy, Elsevier, vol. 133(C).
  45. Chen, Kuilin & Yu, Jie, 2014. "Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach," Applied Energy, Elsevier, vol. 113(C), pages 690-705.
  46. Yu, Jie & Chen, Kuilin & Mori, Junichi & Rashid, Mudassir M., 2013. "A Gaussian mixture copula model based localized Gaussian process regression approach for long-term wind speed prediction," Energy, Elsevier, vol. 61(C), pages 673-686.
  47. Mustafa Kaya, 2019. "A CFD Based Application of Support Vector Regression to Determine the Optimum Smooth Twist for Wind Turbine Blades," Sustainability, MDPI, vol. 11(16), pages 1-25, August.
  48. Shahid, Farah & Zameer, Aneela & Muneeb, Muhammad, 2021. "A novel genetic LSTM model for wind power forecast," Energy, Elsevier, vol. 223(C).
  49. Bouzgou, Hassen & Benoudjit, Nabil, 2011. "Multiple architecture system for wind speed prediction," Applied Energy, Elsevier, vol. 88(7), pages 2463-2471, July.
  50. Carapellucci, Roberto & Giordano, Lorena, 2013. "A methodology for the synthetic generation of hourly wind speed time series based on some known aggregate input data," Applied Energy, Elsevier, vol. 101(C), pages 541-550.
  51. Song, Zhe & Jiang, Yu & Zhang, Zijun, 2014. "Short-term wind speed forecasting with Markov-switching model," Applied Energy, Elsevier, vol. 130(C), pages 103-112.
  52. Yan, Jie & Liu, Yongqian & Han, Shuang & Qiu, Meng, 2013. "Wind power grouping forecasts and its uncertainty analysis using optimized relevance vector machine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 613-621.
  53. Morato, Marcelo M. & Vergara-Dietrich, José & Esparcia, Eugene A. & Ocon, Joey D. & Normey-Rico, Julio E., 2021. "Assessing demand compliance and reliability in the Philippine off-grid islands with Model Predictive Control microgrid coordination," Renewable Energy, Elsevier, vol. 179(C), pages 1271-1290.
  54. Kusiak, Andrew & Zhang, Zijun & Verma, Anoop, 2013. "Prediction, operations, and condition monitoring in wind energy," Energy, Elsevier, vol. 60(C), pages 1-12.
  55. Shafiqur Rehman & Md. Mahbub Alam & Luai M. Alhems & M. Mujahid Rafique, 2018. "Horizontal Axis Wind Turbine Blade Design Methodologies for Efficiency Enhancement—A Review," Energies, MDPI, vol. 11(3), pages 1-34, February.
  56. Hocaoglu, Fatih Onur & Karanfil, Fatih, 2013. "A time series-based approach for renewable energy modeling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 204-214.
  57. Chong, W.T. & Gwani, M. & Shamshirband, S. & Muzammil, W.K. & Tan, C.J. & Fazlizan, A. & Poh, S.C. & Petković, Dalibor & Wong, K.H., 2016. "Application of adaptive neuro-fuzzy methodology for performance investigation of a power-augmented vertical axis wind turbine," Energy, Elsevier, vol. 102(C), pages 630-636.
  58. Rehman, S. & El-Amin, I.M. & Ahmad, F. & Shaahid, S.M. & Al-Shehri, A.M. & Bakhashwain, J.M., 2007. "Wind power resource assessment for Rafha, Saudi Arabia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(5), pages 937-950, June.
  59. Seunghyeon Wang & Hyeonyong Hae & Juhyung Kim, 2018. "Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR," Energies, MDPI, vol. 11(2), pages 1-14, February.
  60. Senkal, Ozan & Kuleli, Tuncay, 2009. "Estimation of solar radiation over Turkey using artificial neural network and satellite data," Applied Energy, Elsevier, vol. 86(7-8), pages 1222-1228, July.
  61. Saeed, Adnan & Li, Chaoshun & Gan, Zhenhao & Xie, Yuying & Liu, Fangjie, 2022. "A simple approach for short-term wind speed interval prediction based on independently recurrent neural networks and error probability distribution," Energy, Elsevier, vol. 238(PC).
  62. Lihki Rubio & Keyla Alba, 2022. "Forecasting Selected Colombian Shares Using a Hybrid ARIMA-SVR Model," Mathematics, MDPI, vol. 10(13), pages 1-21, June.
  63. Ali Arefinia & Omid Bozorg-Haddad & Khaled Ahmadaali & Javad Bazrafshan & Babak Zolghadr-Asli & Xuefeng Chu, 2022. "Estimation of geographical variations in virtual water content and crop yield under climate change: comparison of three data mining approaches," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(6), pages 8378-8396, June.
  64. Zhang, Xiaonei & Zeng, Ming & Meng, Qinghao, 2018. "Multivariate multifractal detrended fluctuation analysis of 3D wind field signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 513-523.
  65. Hu, Weicheng & Yang, Qingshan & Chen, Hua-Peng & Yuan, Ziting & Li, Chen & Shao, Shuai & Zhang, Jian, 2021. "New hybrid approach for short-term wind speed predictions based on preprocessing algorithm and optimization theory," Renewable Energy, Elsevier, vol. 179(C), pages 2174-2186.
  66. Paiho, Satu & Kiljander, Jussi & Sarala, Roope & Siikavirta, Hanne & Kilkki, Olli & Bajpai, Arpit & Duchon, Markus & Pahl, Marc-Oliver & Wüstrich, Lars & Lübben, Christian & Kirdan, Erkin & Schindler,, 2021. "Towards cross-commodity energy-sharing communities – A review of the market, regulatory, and technical situation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
  67. Wei, Zhongbao & Li, Xiaolu & Xu, Lijun & Cheng, Yanting, 2013. "Comparative study of computational intelligence approaches for NOx reduction of coal-fired boiler," Energy, Elsevier, vol. 55(C), pages 683-692.
  68. Sun, Fei & Jin, Tongdan, 2022. "A hybrid approach to multi-step, short-term wind speed forecasting using correlated features," Renewable Energy, Elsevier, vol. 186(C), pages 742-754.
  69. Liu, Jinfu & Ren, Guorui & Wan, Jie & Guo, Yufeng & Yu, Daren, 2016. "Variogram time-series analysis of wind speed," Renewable Energy, Elsevier, vol. 99(C), pages 483-491.
  70. Shah, Kamran Ali & Meng, Fantai & Li, Ye & Nagamune, Ryozo & Zhou, Yarong & Ren, Zhengru & Jiang, Zhiyu, 2021. "A synthesis of feasible control methods for floating offshore wind turbine system dynamics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
  71. Jing Zhao & Yaoqi Duan & Xiaojuan Liu, 2018. "Uncertainty Analysis of Weather Forecast Data for Cooling Load Forecasting Based on the Monte Carlo Method," Energies, MDPI, vol. 11(7), pages 1-18, July.
  72. Liu, Yin & Davanloo Tajbakhsh, Sam & Conejo, Antonio J., 2021. "Spatiotemporal wind forecasting by learning a hierarchically sparse inverse covariance matrix using wind directions," International Journal of Forecasting, Elsevier, vol. 37(2), pages 812-824.
  73. Wei-Chiang Hong & Ping-Feng Pai, 2007. "Potential assessment of the support vector regression technique in rainfall forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(2), pages 495-513, February.
  74. Chen, Han & Huang, Jinhui Jeanne & McBean, Edward, 2020. "Partitioning of daily evapotranspiration using a modified shuttleworth-wallace model, random Forest and support vector regression, for a cabbage farmland," Agricultural Water Management, Elsevier, vol. 228(C).
  75. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
  76. Cai, Haoshu & Jia, Xiaodong & Feng, Jianshe & Yang, Qibo & Li, Wenzhe & Li, Fei & Lee, Jay, 2021. "A unified Bayesian filtering framework for multi-horizon wind speed prediction with improved accuracy," Renewable Energy, Elsevier, vol. 178(C), pages 709-719.
  77. Feng Xing & Xiaoyu Song & Yubo Wang & Caiyan Qin, 2023. "A New Combined Prediction Model for Ultra-Short-Term Wind Power Based on Variational Mode Decomposition and Gradient Boosting Regression Tree," Sustainability, MDPI, vol. 15(14), pages 1-18, July.
  78. Wei-Chiang Hong & Yucheng Dong & Chien-Yuan Lai & Li-Yueh Chen & Shih-Yung Wei, 2011. "SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting," Energies, MDPI, vol. 4(6), pages 1-18, June.
  79. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
  80. Hu, Qinghua & Zhang, Rujia & Zhou, Yucan, 2016. "Transfer learning for short-term wind speed prediction with deep neural networks," Renewable Energy, Elsevier, vol. 85(C), pages 83-95.
  81. Lu, Chi-Jie & Wang, Yen-Wen, 2010. "Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting," International Journal of Production Economics, Elsevier, vol. 128(2), pages 603-613, December.
  82. Shahid, Farah & Zameer, Aneela & Mehmood, Ammara & Raja, Muhammad Asif Zahoor, 2020. "A novel wavenets long short term memory paradigm for wind power prediction," Applied Energy, Elsevier, vol. 269(C).
  83. Muhammad Waseem Ahmad & Anthony Mouraud & Yacine Rezgui & Monjur Mourshed, 2018. "Deep Highway Networks and Tree-Based Ensemble for Predicting Short-Term Building Energy Consumption," Energies, MDPI, vol. 11(12), pages 1-21, December.
  84. Aasim, & Singh, S.N. & Mohapatra, Abheejeet, 2019. "Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting," Renewable Energy, Elsevier, vol. 136(C), pages 758-768.
  85. M. K. Islam & N. M. S. Hassan & M. G. Rasul & Kianoush Emami & Ashfaque Ahmed Chowdhury, 2023. "Forecasting of Solar and Wind Resources for Power Generation," Energies, MDPI, vol. 16(17), pages 1-23, August.
  86. Neeraj Bokde & Andrés Feijóo & Daniel Villanueva & Kishore Kulat, 2019. "A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction," Energies, MDPI, vol. 12(2), pages 1-42, January.
  87. Tascikaraoglu, A. & Uzunoglu, M., 2014. "A review of combined approaches for prediction of short-term wind speed and power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 243-254.
  88. Paniagua-Tineo, A. & Salcedo-Sanz, S. & Casanova-Mateo, C. & Ortiz-García, E.G. & Cony, M.A. & Hernández-Martín, E., 2011. "Prediction of daily maximum temperature using a support vector regression algorithm," Renewable Energy, Elsevier, vol. 36(11), pages 3054-3060.
  89. Danxiang Wei & Jianzhou Wang & Kailai Ni & Guangyu Tang, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network Combined with Fuzzy Time Series for Energy Forecasting," Energies, MDPI, vol. 12(18), pages 1-38, September.
  90. Maria Grazia De Giorgi & Stefano Campilongo & Antonio Ficarella & Paolo Maria Congedo, 2014. "Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)," Energies, MDPI, vol. 7(8), pages 1-22, August.
  91. Carro-Calvo, L. & Salcedo-Sanz, S. & Kirchner-Bossi, N. & Portilla-Figueras, A. & Prieto, L. & Garcia-Herrera, R. & Hernández-Martín, E., 2011. "Extraction of synoptic pressure patterns for long-term wind speed estimation in wind farms using evolutionary computing," Energy, Elsevier, vol. 36(3), pages 1571-1581.
  92. Jiang, Ping & Wang, Yun & Wang, Jianzhou, 2017. "Short-term wind speed forecasting using a hybrid model," Energy, Elsevier, vol. 119(C), pages 561-577.
  93. Xiao, Yulong & Zou, Chongzhe & Chi, Hetian & Fang, Rengcun, 2023. "Boosted GRU model for short-term forecasting of wind power with feature-weighted principal component analysis," Energy, Elsevier, vol. 267(C).
  94. Troncoso, A. & Salcedo-Sanz, S. & Casanova-Mateo, C. & Riquelme, J.C. & Prieto, L., 2015. "Local models-based regression trees for very short-term wind speed prediction," Renewable Energy, Elsevier, vol. 81(C), pages 589-598.
  95. Kusiak, Andrew & Li, Wenyan, 2011. "The prediction and diagnosis of wind turbine faults," Renewable Energy, Elsevier, vol. 36(1), pages 16-23.
  96. Douak, Fouzi & Melgani, Farid & Benoudjit, Nabil, 2013. "Kernel ridge regression with active learning for wind speed prediction," Applied Energy, Elsevier, vol. 103(C), pages 328-340.
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