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Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model

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  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. Li, Chaoshun & Xiao, Zhengguang & Xia, Xin & Zou, Wen & Zhang, Chu, 2018. "A hybrid model based on synchronous optimisation for multi-step short-term wind speed forecasting," Applied Energy, Elsevier, vol. 215(C), pages 131-144.
  3. 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).
  4. 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.
  5. 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.
  6. 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.
  7. Zhang, Yagang & Wang, Hui & Wang, Jingchao & Cheng, Xiaodan & Wang, Tong & Zhao, Zheng, 2024. "Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system," Energy, Elsevier, vol. 292(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. Zhang, Wenyu & Wu, Jie & Wang, Jianzhou & Zhao, Weigang & Shen, Lin, 2012. "Performance analysis of four modified approaches for wind speed forecasting," Applied Energy, Elsevier, vol. 99(C), pages 324-333.
  10. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
  11. Wang, Yun & Wang, Jianzhou & Wei, Xiang, 2015. "A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: A case study of wind farms in northwest China," Energy, Elsevier, vol. 91(C), pages 556-572.
  12. Liu, Hui & Tian, Hong-qi & Liang, Xi-feng & Li, Yan-fei, 2015. "Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks," Applied Energy, Elsevier, vol. 157(C), pages 183-194.
  13. Zhang, Fei & Li, Peng-Cheng & Gao, Lu & Liu, Yong-Qian & Ren, Xiao-Ying, 2021. "Application of autoregressive dynamic adaptive (ARDA) model in real-time wind power forecasting," Renewable Energy, Elsevier, vol. 169(C), pages 129-143.
  14. Arrieta-Prieto, Mario & Schell, Kristen R., 2022. "Spatio-temporal probabilistic forecasting of wind power for multiple farms: A copula-based hybrid model," International Journal of Forecasting, Elsevier, vol. 38(1), pages 300-320.
  15. 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.
  16. Zonggui Yao & Chen Wang, 2018. "A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting," Sustainability, MDPI, vol. 10(5), pages 1-33, May.
  17. Shen, Yun & Mao, Yaqian & Weng, Jiacheng & Wu, Chenxi & Wu, Haixin & Gu, Yangyang & Wang, Jianhong, 2024. "A novel SARCIMA model based on central difference and its application in solar power generation of China," Applied Energy, Elsevier, vol. 360(C).
  18. 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.
  19. Emeksiz, Cem & Tan, Mustafa, 2022. "Wind speed estimation using novelty hybrid adaptive estimation model based on decomposition and deep learning methods (ICEEMDAN-CNN)," Energy, Elsevier, vol. 249(C).
  20. 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).
  21. 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.
  22. 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.
  23. 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.
  24. Abhinav Khare & Qing He & Rajan Batta, 2020. "Predicting gasoline shortage during disasters using social media," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 42(3), pages 693-726, September.
  25. Xu, Weifeng & Liu, Pan & Cheng, Lei & Zhou, Yong & Xia, Qian & Gong, Yu & Liu, Yini, 2021. "Multi-step wind speed prediction by combining a WRF simulation and an error correction strategy," Renewable Energy, Elsevier, vol. 163(C), pages 772-782.
  26. Ruiz-Aguilar, J.J. & Turias, I.J. & Jiménez-Come, M.J., 2014. "Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 67(C), pages 1-13.
  27. Koo, Junmo & Han, Gwon Deok & Choi, Hyung Jong & Shim, Joon Hyung, 2015. "Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: A case study in South Korea," Energy, Elsevier, vol. 93(P2), pages 1296-1302.
  28. 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.
  29. Cheng-Yu Ho & Ke-Sheng Cheng & Chi-Hang Ang, 2023. "Utilizing the Random Forest Method for Short-Term Wind Speed Forecasting in the Coastal Area of Central Taiwan," Energies, MDPI, vol. 16(3), pages 1-18, January.
  30. 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.
  31. Arunraj, Nari Sivanandam & Ahrens, Diane, 2015. "A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 321-335.
  32. Gulay, Emrah & Duru, Okan, 2020. "Hybrid modeling in the predictive analytics of energy systems and prices," Applied Energy, Elsevier, vol. 268(C).
  33. Yulong Bai & Lihong Tang & Manhong Fan & Xiaoyan Ma & Yang Yang, 2020. "Fuzzy First-Order Transition-Rules-Trained Hybrid Forecasting System for Short-Term Wind Speed Forecasts," Energies, MDPI, vol. 13(13), pages 1-21, June.
  34. 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.
  35. Li, Hongmin & Wang, Jianzhou & Lu, Haiyan & Guo, Zhenhai, 2018. "Research and application of a combined model based on variable weight for short term wind speed forecasting," Renewable Energy, Elsevier, vol. 116(PA), pages 669-684.
  36. Liu, Da & Niu, Dongxiao & Wang, Hui & Fan, Leilei, 2014. "Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm," Renewable Energy, Elsevier, vol. 62(C), pages 592-597.
  37. 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.
  38. Xuejiao Ma & Dandan Liu, 2016. "Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting," Energies, MDPI, vol. 9(8), pages 1-34, August.
  39. Wu, Xuedong & Zhu, Zhiyu & Su, Xunliang & Fan, Shaosheng & Du, Zhaoping & Chang, Yanchao & Zeng, Qingjun, 2015. "A study of single multiplicative neuron model with nonlinear filters for hourly wind speed prediction," Energy, Elsevier, vol. 88(C), pages 194-201.
  40. Jiang, Yu & Song, Zhe & Kusiak, Andrew, 2013. "Very short-term wind speed forecasting with Bayesian structural break model," Renewable Energy, Elsevier, vol. 50(C), pages 637-647.
  41. Shukur, Osamah Basheer & Lee, Muhammad Hisyam, 2015. "Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA," Renewable Energy, Elsevier, vol. 76(C), pages 637-647.
  42. Zhang, Shuai & Chen, Yong & Xiao, Jiuhong & Zhang, Wenyu & Feng, Ruijun, 2021. "Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism," Renewable Energy, Elsevier, vol. 174(C), pages 688-704.
  43. 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.
  44. Neeraj Bokde & Andrés Feijóo & Nadhir Al-Ansari & Siyu Tao & Zaher Mundher Yaseen, 2020. "The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling," Energies, MDPI, vol. 13(7), pages 1-23, April.
  45. Liu, Hui & Tian, Hong-qi & Li, Yan-fei, 2012. "Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction," Applied Energy, Elsevier, vol. 98(C), pages 415-424.
  46. 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).
  47. Hernández-Escobedo, Q. & Saldaña-Flores, R. & Rodríguez-García, E.R. & Manzano-Agugliaro, F., 2014. "Wind energy resource in Northern Mexico," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 890-914.
  48. Wang, Cong & Zhang, Hongli & Fan, Wenhui & Ma, Ping, 2017. "A new chaotic time series hybrid prediction method of wind power based on EEMD-SE and full-parameters continued fraction," Energy, Elsevier, vol. 138(C), pages 977-990.
  49. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
  50. Ramasamy, P. & Chandel, S.S. & Yadav, Amit Kumar, 2015. "Wind speed prediction in the mountainous region of India using an artificial neural network model," Renewable Energy, Elsevier, vol. 80(C), pages 338-347.
  51. Erasmo Cadenas & Wilfrido Rivera & Rafael Campos-Amezcua & Christopher Heard, 2016. "Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model," Energies, MDPI, vol. 9(2), pages 1-15, February.
  52. Zendehboudi, Sohrab & Rezaei, Nima & Lohi, Ali, 2018. "Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review," Applied Energy, Elsevier, vol. 228(C), pages 2539-2566.
  53. 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.
  54. 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.
  55. Amir Abdul Majid, 2022. "Forecasting Monthly Wind Energy Using an Alternative Machine Training Method with Curve Fitting and Temporal Error Extraction Algorithm," Energies, MDPI, vol. 15(22), pages 1-24, November.
  56. Liang, Tao & Chai, Chunjie & Sun, Hexu & Tan, Jianxin, 2022. "Wind speed prediction based on multi-variable Capsnet-BILSTM-MOHHO for WPCCC," Energy, Elsevier, vol. 250(C).
  57. Erdong Zhao & Jing Zhao & Liwei Liu & Zhongyue Su & Ning An, 2015. "Hybrid Wind Speed Prediction Based on a Self-Adaptive ARIMAX Model with an Exogenous WRF Simulation," Energies, MDPI, vol. 9(1), pages 1-20, December.
  58. Wang, Jianzhou & Hu, Jianming & Ma, Kailiang & Zhang, Yixin, 2015. "A self-adaptive hybrid approach for wind speed forecasting," Renewable Energy, Elsevier, vol. 78(C), pages 374-385.
  59. 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.
  60. Rahimiyan, Morteza & Morales, Juan M. & Conejo, Antonio J., 2011. "Evaluating alternative offering strategies for wind producers in a pool," Applied Energy, Elsevier, vol. 88(12), pages 4918-4926.
  61. 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.
  62. Wang, Jianzhou & Yang, Wendong & Du, Pei & Li, Yifan, 2018. "Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system," Energy, Elsevier, vol. 148(C), pages 59-78.
  63. 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.
  64. Shi, Jing & Guo, Jinmei & Zheng, Songtao, 2012. "Evaluation of hybrid forecasting approaches for wind speed and power generation time series," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3471-3480.
  65. D׳Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2015. "Reliability measures for indexed semi-Markov chains applied to wind energy production," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 170-177.
  66. Navid Shirzadi & Fuzhan Nasiri & Ramanunni Parakkal Menon & Pilar Monsalvete & Anton Kaifel & Ursula Eicker, 2023. "Smart Urban Wind Power Forecasting: Integrating Weibull Distribution, Recurrent Neural Networks, and Numerical Weather Prediction," Energies, MDPI, vol. 16(17), pages 1-17, August.
  67. 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.
  68. Jiang, Ping & Wang, Yun & Wang, Jianzhou, 2017. "Short-term wind speed forecasting using a hybrid model," Energy, Elsevier, vol. 119(C), pages 561-577.
  69. Sobolewski, Robert Adam & Tchakorom, Médane & Couturier, Raphaël, 2023. "Gradient boosting-based approach for short- and medium-term wind turbine output power prediction," Renewable Energy, Elsevier, vol. 203(C), pages 142-160.
  70. Camelo, Henrique do Nascimento & Lucio, Paulo Sérgio & Leal Junior, João Bosco Verçosa & Carvalho, Paulo Cesar Marques de & Santos, Daniel von Glehn dos, 2018. "Innovative hybrid models for forecasting time series applied in wind generation based on the combination of time series models with artificial neural networks," Energy, Elsevier, vol. 151(C), pages 347-357.
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