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A review on the forecasting of wind speed and generated power

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  1. García, Irene & Huo, Stella & Prado, Raquel & Bravo, Lelys, 2020. "Dynamic Bayesian temporal modeling and forecasting of short-term wind measurements," Renewable Energy, Elsevier, vol. 161(C), pages 55-64.
  2. Zárate-Miñano, Rafael & Anghel, Marian & Milano, Federico, 2013. "Continuous wind speed models based on stochastic differential equations," Applied Energy, Elsevier, vol. 104(C), pages 42-49.
  3. 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.
  4. 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.
  5. Tascikaraoglu, Akin & Sanandaji, Borhan M. & Poolla, Kameshwar & Varaiya, Pravin, 2016. "Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform," Applied Energy, Elsevier, vol. 165(C), pages 735-747.
  6. A. Alexandridis & A. Zapranis, 2013. "Wind Derivatives: Modeling and Pricing," Computational Economics, Springer;Society for Computational Economics, vol. 41(3), pages 299-326, March.
  7. 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).
  8. Tansu Filik, 2016. "Improved Spatio-Temporal Linear Models for Very Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 9(3), pages 1-15, March.
  9. Yin, Shi & Liu, Hui, 2022. "Wind power prediction based on outlier correction, ensemble reinforcement learning, and residual correction," Energy, Elsevier, vol. 250(C).
  10. 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.
  11. 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.
  12. 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.
  13. 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).
  14. Hakan Acaroğlu & Fausto Pedro García Márquez, 2021. "Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy," Energies, MDPI, vol. 14(22), pages 1-23, November.
  15. 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.
  16. Arenas-López, J. Pablo & Badaoui, Mohamed, 2020. "Stochastic modelling of wind speeds based on turbulence intensity," Renewable Energy, Elsevier, vol. 155(C), pages 10-22.
  17. Jiang, Ping & Yang, Hufang & Heng, Jiani, 2019. "A hybrid forecasting system based on fuzzy time series and multi-objective optimization for wind speed forecasting," Applied Energy, Elsevier, vol. 235(C), pages 786-801.
  18. Croonenbroeck, Carsten & Ambach, Daniel, 2014. "A comparison of different wind power forecasting models to the Mycielski approach," Discussion Papers 355, European University Viadrina Frankfurt (Oder), Department of Business Administration and Economics.
  19. 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.
  20. 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.
  21. 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.
  22. De Giorgi, Maria Grazia & Ficarella, Antonio & Tarantino, Marco, 2011. "Assessment of the benefits of numerical weather predictions in wind power forecasting based on statistical methods," Energy, Elsevier, vol. 36(7), pages 3968-3978.
  23. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
  24. Hu, Rui & Hu, Weihao & Gökmen, Nuri & Li, Pengfei & Huang, Qi & Chen, Zhe, 2019. "High resolution wind speed forecasting based on wavelet decomposed phase space reconstruction and self-organizing map," Renewable Energy, Elsevier, vol. 140(C), pages 17-31.
  25. Feng, Cong & Sun, Mucun & Cui, Mingjian & Chartan, Erol Kevin & Hodge, Bri-Mathias & Zhang, Jie, 2019. "Characterizing forecastability of wind sites in the United States," Renewable Energy, Elsevier, vol. 133(C), pages 1352-1365.
  26. Meka, Rajitha & Alaeddini, Adel & Bhaganagar, Kiran, 2021. "A robust deep learning framework for short-term wind power forecast of a full-scale wind farm using atmospheric variables," Energy, Elsevier, vol. 221(C).
  27. Yuya Tanigawa & Narayanan Krishnan & Eitaro Oomine & Atushi Yona & Hiroshi Takahashi & Tomonobu Senjyu, 2023. "Clustering Method for Load Demand to Shorten the Time of Annual Simulation," Energies, MDPI, vol. 16(5), pages 1-22, February.
  28. 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.
  29. Kumar Shivam & Jong-Chyuan Tzou & Shang-Chen Wu, 2020. "Multi-Step Short-Term Wind Speed Prediction Using a Residual Dilated Causal Convolutional Network with Nonlinear Attention," Energies, MDPI, vol. 13(7), pages 1-29, April.
  30. Zheng, Jianqin & Du, Jian & Wang, Bohong & Klemeš, Jiří Jaromír & Liao, Qi & Liang, Yongtu, 2023. "A hybrid framework for forecasting power generation of multiple renewable energy sources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
  31. 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.
  32. Zhou, Yilin & Wang, Jianzhou & Lu, Haiyan & Zhao, Weigang, 2022. "Short-term wind power prediction optimized by multi-objective dragonfly algorithm based on variational mode decomposition," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
  33. 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).
  34. Daniel Ambach & Carsten Croonenbroeck, 2016. "Space-time short- to medium-term wind speed forecasting," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 5-20, March.
  35. 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).
  36. 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.
  37. Narayana, Mahinsasa & Sunderland, Keith M. & Putrus, Ghanim & Conlon, Michael F., 2017. "Adaptive linear prediction for optimal control of wind turbines," Renewable Energy, Elsevier, vol. 113(C), pages 895-906.
  38. Minaeian, Ali & Sedaghat, Ahmad & Mostafaeipour, Ali & Akbar Alemrajabi, Ali, 2017. "Exploring economy of small communities and households by investing on harnessing wind energy in the province of Sistan-Baluchestan in Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 835-847.
  39. Lazić, Lazar & Pejanović, Goran & Živković, Momčilo & Ilić, Luka, 2014. "Improved wind forecasts for wind power generation using the Eta model and MOS (Model Output Statistics) method," Energy, Elsevier, vol. 73(C), pages 567-574.
  40. 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.
  41. 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.
  42. Fosso Wamba, Samuel & Bawack, Ransome Epie & Guthrie, Cameron & Queiroz, Maciel M. & Carillo, Kevin Daniel André, 2021. "Are we preparing for a good AI society? A bibliometric review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
  43. Rasku, Topi & Miettinen, Jari & Rinne, Erkka & Kiviluoma, Juha, 2020. "Impact of 15-day energy forecasts on the hydro-thermal scheduling of a future Nordic power system," Energy, Elsevier, vol. 192(C).
  44. Wang, Yun & Xu, Houhua & Zou, Runmin & Zhang, Fan & Hu, Qinghua, 2024. "Dynamic non-constraint ensemble model for probabilistic wind power and wind speed forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 204(C).
  45. Michael F. Howland & John O. Dabiri, 2019. "Wind Farm Modeling with Interpretable Physics-Informed Machine Learning," Energies, MDPI, vol. 12(14), pages 1-21, July.
  46. Wang, Yun & Wang, Haibo & Srinivasan, Dipti & Hu, Qinghua, 2019. "Robust functional regression for wind speed forecasting based on Sparse Bayesian learning," Renewable Energy, Elsevier, vol. 132(C), pages 43-60.
  47. Saira Al-Zadjali & Ahmed Al Maashri & Amer Al-Hinai & Sultan Al-Yahyai & Mostafa Bakhtvar, 2019. "An Accurate, Light-Weight Wind Speed Predictor for Renewable Energy Management Systems," Energies, MDPI, vol. 12(22), pages 1-20, November.
  48. K. R. Sri Preethaa & Akila Muthuramalingam & Yuvaraj Natarajan & Gitanjali Wadhwa & Ahmed Abdi Yusuf Ali, 2023. "A Comprehensive Review on Machine Learning Techniques for Forecasting Wind Flow Pattern," Sustainability, MDPI, vol. 15(17), pages 1-22, August.
  49. Liu, Lei & Liu, Jicheng & Ye, Yu & Liu, Hui & Chen, Kun & Li, Dong & Dong, Xue & Sun, Mingzhai, 2023. "Ultra-short-term wind power forecasting based on deep Bayesian model with uncertainty," Renewable Energy, Elsevier, vol. 205(C), pages 598-607.
  50. 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.
  51. Akintayo Temiloluwa Abolude & Wen Zhou, 2018. "Assessment and Performance Evaluation of a Wind Turbine Power Output," Energies, MDPI, vol. 11(8), pages 1-15, August.
  52. Hufang Yang & Zaiping Jiang & Haiyan Lu, 2017. "A Hybrid Wind Speed Forecasting System Based on a ‘Decomposition and Ensemble’ Strategy and Fuzzy Time Series," Energies, MDPI, vol. 10(9), pages 1-30, September.
  53. 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.
  54. Makky, Ahmed Al & Alaswad, A & Gibson, Desmond & Olabi, A.G, 2017. "Renewable energy scenario and environmental aspects of soil emission measurements," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P2), pages 1157-1173.
  55. 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.
  56. 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.
  57. Zhou, Kaile & Fu, Chao & Yang, Shanlin, 2016. "Big data driven smart energy management: From big data to big insights," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 215-225.
  58. Gupta, R.A. & Kumar, Rajesh & Bansal, Ajay Kumar, 2015. "BBO-based small autonomous hybrid power system optimization incorporating wind speed and solar radiation forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1366-1375.
  59. Nima Amjady & Oveis Abedinia, 2017. "Short Term Wind Power Prediction Based on Improved Kriging Interpolation, Empirical Mode Decomposition, and Closed-Loop Forecasting Engine," Sustainability, MDPI, vol. 9(11), pages 1-18, November.
  60. Li Bai & Pierre Pinson, 2019. "Distributed Reconciliation in Day-Ahead Wind Power Forecasting," Energies, MDPI, vol. 12(6), pages 1-19, March.
  61. Hao Wang & Chen Peng & Bolin Liao & Xinwei Cao & Shuai Li, 2023. "Wind Power Forecasting Based on WaveNet and Multitask Learning," Sustainability, MDPI, vol. 15(14), pages 1-22, July.
  62. Dinler, Ali, 2013. "A new low-correlation MCP (measure-correlate-predict) method for wind energy forecasting," Energy, Elsevier, vol. 63(C), pages 152-160.
  63. Namrye Son & Seunghak Yang & Jeongseung Na, 2019. "Hybrid Forecasting Model for Short-Term Wind Power Prediction Using Modified Long Short-Term Memory," Energies, MDPI, vol. 12(20), pages 1-17, October.
  64. Wang, Deyun & Luo, Hongyuan & Grunder, Olivier & Lin, Yanbing & Guo, Haixiang, 2017. "Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm," Applied Energy, Elsevier, vol. 190(C), pages 390-407.
  65. Hugo T. V. Gouveia & Murilo A. Souza & Aida A. Ferreira & Jonata C. de Albuquerque & Otoni Nóbrega Neto & Milde Maria da Silva Lira & Ronaldo R. B. de Aquino, 2023. "Application of Augmented Echo State Networks and Genetic Algorithm to Improve Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 16(6), pages 1-15, March.
  66. Wang, Tian & Deng, Shiming, 2019. "Multi-Period energy procurement policies for smart-grid communities with deferrable demand and supplementary uncertain power supplies," Omega, Elsevier, vol. 89(C), pages 212-226.
  67. Anup Suryawanshi & Debraj Ghosh, 2015. "Wind speed prediction using spatio-temporal covariance," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(2), pages 1435-1449, January.
  68. Daniel Ambach & Carsten Croonenbroeck, 2016. "Space-time short- to medium-term wind speed forecasting," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 5-20, March.
  69. Bigdeli, Nooshin & Afshar, Karim & Gazafroudi, Amin Shokri & Ramandi, Mostafa Yousefi, 2013. "A comparative study of optimal hybrid methods for wind power prediction in wind farm of Alberta, Canada," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 20-29.
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  71. Jurasz, Jakub & Dąbek, Paweł B. & Kaźmierczak, Bartosz & Kies, Alexander & Wdowikowski, Marcin, 2018. "Large scale complementary solar and wind energy sources coupled with pumped-storage hydroelectricity for Lower Silesia (Poland)," Energy, Elsevier, vol. 161(C), pages 183-192.
  72. 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.
  73. Neves, Diana & Brito, Miguel C. & Silva, Carlos A., 2016. "Impact of solar and wind forecast uncertainties on demand response of isolated microgrids," Renewable Energy, Elsevier, vol. 87(P2), pages 1003-1015.
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  75. Prakash, Om & Laguri, Vinod & Pandey, Anukul & Kumar, Anil & Kumar, Arbind, 2016. "Review on various modelling techniques for the solar dryers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 396-417.
  76. Manoj Verma & Harish Kumar Ghritlahre & Ghrithanchi Chandrakar, 2023. "Wind Speed Prediction of Central Region of Chhattisgarh (India) Using Artificial Neural Network and Multiple Linear Regression Technique: A Comparative Study," Annals of Data Science, Springer, vol. 10(4), pages 851-873, August.
  77. De Giorgi, Maria Grazia & Ficarella, Antonio & Tarantino, Marco, 2011. "Error analysis of short term wind power prediction models," Applied Energy, Elsevier, vol. 88(4), pages 1298-1311, April.
  78. Jónsdóttir, Guðrún Margrét & Milano, Federico, 2019. "Data-based continuous wind speed models with arbitrary probability distribution and autocorrelation," Renewable Energy, Elsevier, vol. 143(C), pages 368-376.
  79. D’Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2015. "Economic performance indicators of wind energy based on wind speed stochastic modeling," Applied Energy, Elsevier, vol. 154(C), pages 290-297.
  80. David Barbosa de Alencar & Carolina De Mattos Affonso & Roberto Célio Limão de Oliveira & Jorge Laureano Moya Rodríguez & Jandecy Cabral Leite & José Carlos Reston Filho, 2017. "Different Models for Forecasting Wind Power Generation: Case Study," Energies, MDPI, vol. 10(12), pages 1-27, November.
  81. Yeojin Kim & Jin Hur, 2020. "An Ensemble Forecasting Model of Wind Power Outputs Based on Improved Statistical Approaches," Energies, MDPI, vol. 13(5), pages 1-11, March.
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  83. 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.
  84. Verdejo, Humberto & Awerkin, Almendra & Saavedra, Eugenio & Kliemann, Wolfgang & Vargas, Luis, 2016. "Stochastic modeling to represent wind power generation and demand in electric power system based on real data," Applied Energy, Elsevier, vol. 173(C), pages 283-295.
  85. Chen, Jun & Rabiti, Cristian, 2017. "Synthetic wind speed scenarios generation for probabilistic analysis of hybrid energy systems," Energy, Elsevier, vol. 120(C), pages 507-517.
  86. Yanbing Lin & Hongyuan Luo & Deyun Wang & Haixiang Guo & Kejun Zhu, 2017. "An Ensemble Model Based on Machine Learning Methods and Data Preprocessing for Short-Term Electric Load Forecasting," Energies, MDPI, vol. 10(8), pages 1-16, August.
  87. Jafarzadeh Ghoushchi, Saeid & Manjili, Sobhan & Mardani, Abbas & Saraji, Mahyar Kamali, 2021. "An extended new approach for forecasting short-term wind power using modified fuzzy wavelet neural network: A case study in wind power plant," Energy, Elsevier, vol. 223(C).
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