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Wind speed forecasting for wind farms: A method based on support vector regression

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  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. 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.
  3. Jurasz, Jakub & Mikulik, Jerzy & Krzywda, Magdalena & Ciapała, Bartłomiej & Janowski, Mirosław, 2018. "Integrating a wind- and solar-powered hybrid to the power system by coupling it with a hydroelectric power station with pumping installation," Energy, Elsevier, vol. 144(C), pages 549-563.
  4. Jakub Jurasz & Jerzy Mikulik, 2017. "A strategy for the photovoltaic-powered pumped storage hydroelectricity," Energy & Environment, , vol. 28(5-6), pages 544-563, September.
  5. Mostafa Farrokhabadi, 2019. "Data-Driven Mitigation of Energy Scheduling Inaccuracy in Renewable-Penetrated Grids: Summerside Electric Use Case," Energies, MDPI, vol. 12(12), pages 1-23, June.
  6. 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.
  7. 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.
  8. 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.
  9. Wu, Zhuochun & Xia, Xiangjie & Xiao, Liye & Liu, Yilin, 2020. "Combined model with secondary decomposition-model selection and sample selection for multi-step wind power forecasting," Applied Energy, Elsevier, vol. 261(C).
  10. Alvarez-Mendoza, Fernanda & Bacher, Peder & Madsen, Henrik & Angeles-Camacho, César, 2017. "Stochastic model of wind-fuel cell for a semi-dispatchable power generation," Applied Energy, Elsevier, vol. 193(C), pages 139-148.
  11. 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).
  12. 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.
  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. 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.
  15. 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.
  16. Méndez-Gordillo, Alma Rosa & Cadenas, Erasmo, 2021. "Wind speed forecasting by the extraction of the multifractal patterns of time series through the multiplicative cascade technique," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
  17. Lin, Boqiang & Zhang, Chongchong, 2021. "A novel hybrid machine learning model for short-term wind speed prediction in inner Mongolia, China," Renewable Energy, Elsevier, vol. 179(C), pages 1565-1577.
  18. Méndez-Gordillo, Alma Rosa & Campos-Amezcua, Rafael & Cadenas, Erasmo, 2022. "Wind speed forecasting using a hybrid model considering the turbulence of the airflow," Renewable Energy, Elsevier, vol. 196(C), pages 422-431.
  19. 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.
  20. 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).
  21. Hu, Jianming & Heng, Jiani & Wen, Jiemei & Zhao, Weigang, 2020. "Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm," Renewable Energy, Elsevier, vol. 162(C), pages 1208-1226.
  22. Shengli Liao & Xudong Tian & Benxi Liu & Tian Liu & Huaying Su & Binbin Zhou, 2022. "Short-Term Wind Power Prediction Based on LightGBM and Meteorological Reanalysis," Energies, MDPI, vol. 15(17), pages 1-21, August.
  23. 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.
  24. 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).
  25. 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.
  26. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
  27. Cai, Haoshu & Jia, Xiaodong & Feng, Jianshe & Li, Wenzhe & Hsu, Yuan-Ming & Lee, Jay, 2020. "Gaussian Process Regression for numerical wind speed prediction enhancement," Renewable Energy, Elsevier, vol. 146(C), pages 2112-2123.
  28. Yang, YouLong & Che, JinXing & Li, YanYing & Zhao, YanJun & Zhu, SuLing, 2016. "An incremental electric load forecasting model based on support vector regression," Energy, Elsevier, vol. 113(C), pages 796-808.
  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. Purohit, Shantanu & Ng, E.Y.K. & Syed Ahmed Kabir, Ijaz Fazil, 2022. "Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake," Renewable Energy, Elsevier, vol. 184(C), pages 405-420.
  32. Liu, Guanjun & Wang, Yun & Qin, Hui & Shen, Keyan & Liu, Shuai & Shen, Qin & Qu, Yuhua & Zhou, Jianzhong, 2023. "Probabilistic spatiotemporal forecasting of wind speed based on multi-network deep ensembles method," Renewable Energy, Elsevier, vol. 209(C), pages 231-247.
  33. Wang, Jujie & Li, Yaning, 2018. "Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy," Applied Energy, Elsevier, vol. 230(C), pages 429-443.
  34. Liu, Tongxiang & Zhao, Qiujun & Wang, Jianzhou & Gao, Yuyang, 2021. "A novel interval forecasting system for uncertainty modeling based on multi-input multi-output theory: A case study on modern wind stations," Renewable Energy, Elsevier, vol. 163(C), pages 88-104.
  35. Croonenbroeck, Carsten & Stadtmann, Georg, 2019. "Renewable generation forecast studies – Review and good practice guidance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 312-322.
  36. Guanjun Liu & Chao Wang & Hui Qin & Jialong Fu & Qin Shen, 2022. "A Novel Hybrid Machine Learning Model for Wind Speed Probabilistic Forecasting," Energies, MDPI, vol. 15(19), pages 1-16, September.
  37. 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).
  38. 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.
  39. Huang, Yu & Zhang, Bingzhe & Pang, Huizhen & Wang, Biao & Lee, Kwang Y. & Xie, Jiale & Jin, Yupeng, 2022. "Spatio-temporal wind speed prediction based on Clayton Copula function with deep learning fusion," Renewable Energy, Elsevier, vol. 192(C), pages 526-536.
  40. Duan, Jikai & Zuo, Hongchao & Bai, Yulong & Duan, Jizheng & Chang, Mingheng & Chen, Bolong, 2021. "Short-term wind speed forecasting using recurrent neural networks with error correction," Energy, Elsevier, vol. 217(C).
  41. Costa, Marcelo Azevedo & Ruiz-Cárdenas, Ramiro & Mineti, Leandro Brioschi & Prates, Marcos Oliveira, 2021. "Dynamic time scan forecasting for multi-step wind speed prediction," Renewable Energy, Elsevier, vol. 177(C), pages 584-595.
  42. He, Ruiyang & Yang, Hongxing & Sun, Shilin & Lu, Lin & Sun, Haiying & Gao, Xiaoxia, 2022. "A machine learning-based fatigue loads and power prediction method for wind turbines under yaw control," Applied Energy, Elsevier, vol. 326(C).
  43. 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.
  44. Ke Zhang & Xiao Li & Jie Su, 2022. "Variable Support Segment-Based Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 15(11), pages 1-18, June.
  45. Wang, Sen & Qin, Chaoxu & Feng, Qihong & Javadpour, Farzam & Rui, Zhenhua, 2021. "A framework for predicting the production performance of unconventional resources using deep learning," Applied Energy, Elsevier, vol. 295(C).
  46. 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.
  47. 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.
  48. Muhammad Jabir & Hazlee Azil Illias & Safdar Raza & Hazlie Mokhlis, 2017. "Intermittent Smoothing Approaches for Wind Power Output: A Review," Energies, MDPI, vol. 10(10), pages 1-23, October.
  49. Wei Dong & Qiang Yang & Xinli Fang, 2018. "Multi-Step Ahead Wind Power Generation Prediction Based on Hybrid Machine Learning Techniques," Energies, MDPI, vol. 11(8), pages 1-19, July.
  50. Herbert Amezquita & Pedro M. S. Carvalho & Hugo Morais, 2023. "Wind Forecast at Medium Voltage Distribution Networks," Energies, MDPI, vol. 16(6), pages 1-23, March.
  51. Duan, Jikai & Chang, Mingheng & Chen, Xiangyue & Wang, Wenpeng & Zuo, Hongchao & Bai, Yulong & Chen, Bolong, 2022. "A combined short-term wind speed forecasting model based on CNN–RNN and linear regression optimization considering error," Renewable Energy, Elsevier, vol. 200(C), pages 788-808.
  52. 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.
  53. Qunli Wu & Huaxing Lin, 2019. "Short-Term Wind Speed Forecasting Based on Hybrid Variational Mode Decomposition and Least Squares Support Vector Machine Optimized by Bat Algorithm Model," Sustainability, MDPI, vol. 11(3), pages 1-18, January.
  54. Cuadra, L. & Ocampo-Estrella, I. & Alexandre, E. & Salcedo-Sanz, S., 2019. "A study on the impact of easements in the deployment of wind farms near airport facilities," Renewable Energy, Elsevier, vol. 135(C), pages 566-588.
  55. Hossein Hassani & Emmanuel S. Silva & Marine Combe & Demetra Andreou & Mansi Ghodsi & Mohammad Reza Yeganegi & Rodolphe E. Gozlan, 2019. "A Support Vector Machine Based Approach for Predicting the Risk of Freshwater Disease Emergence in England," Stats, MDPI, vol. 2(1), pages 1-15, February.
  56. Jian Yang & Xin Zhao & Haikun Wei & Kanjian Zhang, 2019. "Sample Selection Based on Active Learning for Short-Term Wind Speed Prediction," Energies, MDPI, vol. 12(3), pages 1-12, January.
  57. Juan. J. Flores & José R. Cedeño González & Héctor Rodríguez & Mario Graff & Rodrigo Lopez-Farias & Felix Calderon, 2019. "Soft Computing Methods with Phase Space Reconstruction for Wind Speed Forecasting—A Performance Comparison," Energies, MDPI, vol. 12(18), pages 1-19, September.
  58. Jianguo Zhou & Xuechao Yu & Baoling Jin, 2018. "Short-Term Wind Power Forecasting: A New Hybrid Model Combined Extreme-Point Symmetric Mode Decomposition, Extreme Learning Machine and Particle Swarm Optimization," Sustainability, MDPI, vol. 10(9), pages 1-18, September.
  59. Jingyue Wang & Zheng Qian & Jingyi Wang & Yan Pei, 2020. "Hour-Ahead Photovoltaic Power Forecasting Using an Analog Plus Neural Network Ensemble Method," Energies, MDPI, vol. 13(12), pages 1-17, June.
  60. Chang, G.W. & Lu, H.J. & Chang, Y.R. & Lee, Y.D., 2017. "An improved neural network-based approach for short-term wind speed and power forecast," Renewable Energy, Elsevier, vol. 105(C), pages 301-311.
  61. Lu, Peng & Ye, Lin & Tang, Yong & Zhao, Yongning & Zhong, Wuzhi & Qu, Ying & Zhai, Bingxu, 2021. "Ultra-short-term combined prediction approach based on kernel function switch mechanism," Renewable Energy, Elsevier, vol. 164(C), pages 842-866.
  62. Alberto-Jesus Perea-Moreno & Gerardo Alcalá & Quetzalcoatl Hernandez-Escobedo, 2019. "Seasonal Wind Energy Characterization in the Gulf of Mexico," Energies, MDPI, vol. 13(1), pages 1-21, December.
  63. Bai, Yulong & Liu, Ming-De & Ding, Lin & Ma, Yong-Jie, 2021. "Double-layer staged training echo-state networks for wind speed prediction using variational mode decomposition," Applied Energy, Elsevier, vol. 301(C).
  64. Zheng, Hanbo & Huang, Wufeng & Zhao, Junhui & Liu, Jiefeng & Zhang, Yiyi & Shi, Zhen & Zhang, Chaohai, 2022. "A novel falling model for wind speed probability distribution of wind farms," Renewable Energy, Elsevier, vol. 184(C), pages 91-99.
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