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A review on the young history of the wind power short-term prediction

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  1. Zhang, Yao & Wang, Jianxue & Wang, Xifan, 2014. "Review on probabilistic forecasting of wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 255-270.
  2. 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.
  3. Croonenbroeck, Carsten & Møller Dahl, Christian, 2014. "Accurate medium-term wind power forecasting in a censored classification framework," Discussion Papers 351, European University Viadrina Frankfurt (Oder), Department of Business Administration and Economics.
  4. Yuan Sun & Shiyang Zhang, 2024. "A Multiscale Hybrid Wind Power Prediction Model Based on Least Squares Support Vector Regression–Regularized Extreme Learning Machine–Multi-Head Attention–Bidirectional Gated Recurrent Unit and Data D," Energies, MDPI, vol. 17(12), pages 1-21, June.
  5. Blonbou, Ruddy, 2011. "Very short-term wind power forecasting with neural networks and adaptive Bayesian learning," Renewable Energy, Elsevier, vol. 36(3), pages 1118-1124.
  6. Chitsazan, Mohammad Amin & Sami Fadali, M. & Trzynadlowski, Andrzej M., 2019. "Wind speed and wind direction forecasting using echo state network with nonlinear functions," Renewable Energy, Elsevier, vol. 131(C), pages 879-889.
  7. Sameer Al-Dahidi & Piero Baraldi & Enrico Zio & Lorenzo Montelatici, 2021. "Bootstrapped Ensemble of Artificial Neural Networks Technique for Quantifying Uncertainty in Prediction of Wind Energy Production," Sustainability, MDPI, vol. 13(11), pages 1-19, June.
  8. Dehua Zheng & Min Shi & Yifeng Wang & Abinet Tesfaye Eseye & Jianhua Zhang, 2017. "Day-Ahead Wind Power Forecasting Using a Two-Stage Hybrid Modeling Approach Based on SCADA and Meteorological Information, and Evaluating the Impact of Input-Data Dependency on Forecasting Accuracy," Energies, MDPI, vol. 10(12), pages 1-23, December.
  9. Croonenbroeck, Carsten & Dahl, Christian Møller, 2014. "Accurate medium-term wind power forecasting in a censored classification framework," Energy, Elsevier, vol. 73(C), pages 221-232.
  10. Yonggang Li & Yue Wang & Binyuan Wu, 2020. "Short-Term Direct Probability Prediction Model of Wind Power Based on Improved Natural Gradient Boosting," Energies, MDPI, vol. 13(18), pages 1-15, September.
  11. Croonenbroeck, Carsten & Ambach, Daniel, 2014. "Censored spatial wind power prediction with random effects," Discussion Papers 362, European University Viadrina Frankfurt (Oder), Department of Business Administration and Economics.
  12. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Nissen, J.N., 2015. "A novel application of an analog ensemble for short-term wind power forecasting," Renewable Energy, Elsevier, vol. 76(C), pages 768-781.
  13. Salcedo-Sanz, S. & Cornejo-Bueno, L. & Prieto, L. & Paredes, D. & García-Herrera, R., 2018. "Feature selection in machine learning prediction systems for renewable energy applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 728-741.
  14. Cadenas, E. & Jaramillo, O.A. & Rivera, W., 2010. "Analysis and forecasting of wind velocity in chetumal, quintana roo, using the single exponential smoothing method," Renewable Energy, Elsevier, vol. 35(5), pages 925-930.
  15. Gallego, C. & Pinson, P. & Madsen, H. & Costa, A. & Cuerva, A., 2011. "Influence of local wind speed and direction on wind power dynamics – Application to offshore very short-term forecasting," Applied Energy, Elsevier, vol. 88(11), pages 4087-4096.
  16. Naik, Jyotirmayee & Dash, Sujit & Dash, P.K. & Bisoi, Ranjeeta, 2018. "Short term wind power forecasting using hybrid variational mode decomposition and multi-kernel regularized pseudo inverse neural network," Renewable Energy, Elsevier, vol. 118(C), pages 180-212.
  17. Croonenbroeck, Carsten & Ambach, Daniel, 2015. "Censored spatial wind power prediction with random effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 613-622.
  18. Nikodinoska, Dragana & Käso, Mathias & Müsgens, Felix, 2022. "Solar and wind power generation forecasts using elastic net in time-varying forecast combinations," Applied Energy, Elsevier, vol. 306(PA).
  19. Gallego-Castillo, Cristobal & Cuerva-Tejero, Alvaro & Lopez-Garcia, Oscar, 2015. "A review on the recent history of wind power ramp forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1148-1157.
  20. Samet, Haidar & Marzbani, Fatemeh, 2014. "Quantizing the deterministic nonlinearity in wind speed time series," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 1143-1154.
  21. 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.
  22. Liu, Jinqiang & Wang, Xiaoru & Lu, Yun, 2017. "A novel hybrid methodology for short-term wind power forecasting based on adaptive neuro-fuzzy inference system," Renewable Energy, Elsevier, vol. 103(C), pages 620-629.
  23. Ning Li & Fuxing He & Wentao Ma, 2019. "Wind Power Prediction Based on Extreme Learning Machine with Kernel Mean p -Power Error Loss," Energies, MDPI, vol. 12(4), pages 1-19, February.
  24. Iversen, Emil B. & Morales, Juan M. & Møller, Jan K. & Madsen, Henrik, 2016. "Short-term probabilistic forecasting of wind speed using stochastic differential equations," International Journal of Forecasting, Elsevier, vol. 32(3), pages 981-990.
  25. Giabardo, Paolo & Zugno, Marco & Pinson, Pierre & Madsen, Henrik, 2010. "Feedback, competition and stochasticity in a day ahead electricity market," Energy Economics, Elsevier, vol. 32(2), pages 292-301, March.
  26. Munteanu, Iulian & Besançon, Gildas, 2016. "Identification-based prediction of wind park power generation," Renewable Energy, Elsevier, vol. 97(C), pages 422-433.
  27. Yao Zhang & Fan Lin & Ke Wang, 2020. "Robustness of Short-Term Wind Power Forecasting against False Data Injection Attacks," Energies, MDPI, vol. 13(15), pages 1-21, July.
  28. 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.
  29. 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.
  30. Nielsen, Jannie Jessen & Sørensen, John Dalsgaard, 2011. "On risk-based operation and maintenance of offshore wind turbine components," Reliability Engineering and System Safety, Elsevier, vol. 96(1), pages 218-229.
  31. 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.
  32. Hoolohan, Victoria & Tomlin, Alison S. & Cockerill, Timothy, 2018. "Improved near surface wind speed predictions using Gaussian process regression combined with numerical weather predictions and observed meteorological data," Renewable Energy, Elsevier, vol. 126(C), pages 1043-1054.
  33. Jannet Jamii & Mohamed Trabelsi & Majdi Mansouri & Mohamed Fouazi Mimouni & Wasfi Shatanawi, 2022. "Non-Linear Programming-Based Energy Management for a Wind Farm Coupled with Pumped Hydro Storage System," Sustainability, MDPI, vol. 14(18), pages 1-17, September.
  34. Sánchez, Ismael, 2011. "Densidad de predicción basada en momentos condicionados y máxima entropía : aplicación a la predicción de potencia eólica," DES - Working Papers. Statistics and Econometrics. WS ws111813, Universidad Carlos III de Madrid. Departamento de Estadística.
  35. Yan, Jie & Liu, Yongqian & Han, Shuang & Wang, Yimei & Feng, Shuanglei, 2015. "Reviews on uncertainty analysis of wind power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1322-1330.
  36. Sun, Gaiping & Jiang, Chuanwen & Cheng, Pan & Liu, Yangyang & Wang, Xu & Fu, Yang & He, Yang, 2018. "Short-term wind power forecasts by a synthetical similar time series data mining method," Renewable Energy, Elsevier, vol. 115(C), pages 575-584.
  37. Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, July.
  38. Antonio Bracale & Pasquale De Falco, 2015. "An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power," Energies, MDPI, vol. 8(9), pages 1-22, September.
  39. Osório, G.J. & Matias, J.C.O. & Catalão, J.P.S., 2015. "Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information," Renewable Energy, Elsevier, vol. 75(C), pages 301-307.
  40. Bayón, L. & Grau, J.M. & Ruiz, M.M. & Suárez, P.M., 2016. "A comparative economic study of two configurations of hydro-wind power plants," Energy, Elsevier, vol. 112(C), pages 8-16.
  41. Talari, Saber & Shafie-khah, Miadreza & Osório, Gerardo J. & Aghaei, Jamshid & Catalão, João P.S., 2018. "Stochastic modelling of renewable energy sources from operators' point-of-view: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1953-1965.
  42. 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.
  43. Mauritzen, Johannes, 2012. "Dead Battery? Wind Power, the Spot Market, and Hydro Power Interaction in the Nordic Electricity Market," Working Paper Series 908, Research Institute of Industrial Economics.
  44. Khatereh Ghasvarian Jahromi & Davood Gharavian & Hamid Reza Mahdiani, 2023. "Wind power prediction based on wind speed forecast using hidden Markov model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 101-123, January.
  45. Wang, Shuangxin & Shi, Jiarong & Yang, Wei & Yin, Qingyan, 2024. "High and low frequency wind power prediction based on Transformer and BiGRU-Attention," Energy, Elsevier, vol. 288(C).
  46. 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.
  47. 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.
  48. Laura Cornejo-Bueno & Lucas Cuadra & Silvia Jiménez-Fernández & Javier Acevedo-Rodríguez & Luis Prieto & Sancho Salcedo-Sanz, 2017. "Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data," Energies, MDPI, vol. 10(11), pages 1-27, November.
  49. Pinson, P. & Nielsen, H.Aa. & Madsen, H. & Kariniotakis, G., 2009. "Skill forecasting from ensemble predictions of wind power," Applied Energy, Elsevier, vol. 86(7-8), pages 1326-1334, July.
  50. Jung, Sungmoon & Arda Vanli, O. & Kwon, Soon-Duck, 2013. "Wind energy potential assessment considering the uncertainties due to limited data," Applied Energy, Elsevier, vol. 102(C), pages 1492-1503.
  51. Ramirez-Rosado, Ignacio J. & Fernandez-Jimenez, L. Alfredo & Monteiro, Cláudio & Sousa, João & Bessa, Ricardo, 2009. "Comparison of two new short-term wind-power forecasting systems," Renewable Energy, Elsevier, vol. 34(7), pages 1848-1854.
  52. Monteiro, Claudio & Ramirez-Rosado, Ignacio J. & Fernandez-Jimenez, L. Alfredo, 2013. "Short-term forecasting model for electric power production of small-hydro power plants," Renewable Energy, Elsevier, vol. 50(C), pages 387-394.
  53. Fusco, Francesco & Nolan, Gary & Ringwood, John V., 2010. "Variability reduction through optimal combination of wind/wave resources – An Irish case study," Energy, Elsevier, vol. 35(1), pages 314-325.
  54. Poncela, Marta & Poncela, Pilar & Perán, José Ramón, 2013. "Automatic tuning of Kalman filters by maximum likelihood methods for wind energy forecasting," Applied Energy, Elsevier, vol. 108(C), pages 349-362.
  55. Wang, Yibo & Shao, Xinyao & Liu, Chuang & Cai, Guowei & Kou, Lei & Wu, Zhiqiang, 2019. "Analysis of wind farm output characteristics based on descriptive statistical analysis and envelope domain," Energy, Elsevier, vol. 170(C), pages 580-591.
  56. González-Sopeña, J.M. & Pakrashi, V. & Ghosh, B., 2021. "An overview of performance evaluation metrics for short-term statistical wind power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
  57. 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.
  58. Haque, Ashraf U. & Mandal, Paras & Kaye, Mary E. & Meng, Julian & Chang, Liuchen & Senjyu, Tomonobu, 2012. "A new strategy for predicting short-term wind speed using soft computing models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4563-4573.
  59. Peng Lu & Lin Ye & Bohao Sun & Cihang Zhang & Yongning Zhao & Jingzhu Teng, 2018. "A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA," Energies, MDPI, vol. 11(4), pages 1-23, March.
  60. Wu, Jie & Wang, Zhi-Xin & Wang, Guo-Qiang, 2014. "The key technologies and development of offshore wind farm in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 453-462.
  61. Ziel, Florian & Croonenbroeck, Carsten & Ambach, Daniel, 2016. "Forecasting wind power – Modeling periodic and non-linear effects under conditional heteroscedasticity," Applied Energy, Elsevier, vol. 177(C), pages 285-297.
  62. 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.
  63. Han, Li & Romero, Carlos E. & Yao, Zheng, 2015. "Wind power forecasting based on principle component phase space reconstruction," Renewable Energy, Elsevier, vol. 81(C), pages 737-744.
  64. Kou, Peng & Gao, Feng & Guan, Xiaohong, 2013. "Sparse online warped Gaussian process for wind power probabilistic forecasting," Applied Energy, Elsevier, vol. 108(C), pages 410-428.
  65. Samuel Atuahene & Yukun Bao & Yao Yevenyo Ziggah & Patricia Semwaah Gyan & Feng Li, 2018. "Short-Term Electric Power Forecasting Using Dual-Stage Hierarchical Wavelet- Particle Swarm Optimization- Adaptive Neuro-Fuzzy Inference System PSO-ANFIS Approach Based On Climate Change," Energies, MDPI, vol. 11(10), pages 1-19, October.
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  83. Pinson, P. & Girard, R., 2012. "Evaluating the quality of scenarios of short-term wind power generation," Applied Energy, Elsevier, vol. 96(C), pages 12-20.
  84. Chehouri, Adam & Younes, Rafic & Ilinca, Adrian & Perron, Jean, 2015. "Review of performance optimization techniques applied to wind turbines," Applied Energy, Elsevier, vol. 142(C), pages 361-388.
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