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Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA

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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. Li, Min & Yang, Yi & He, Zhaoshuang & Guo, Xinbo & Zhang, Ruisheng & Huang, Bingqing, 2023. "A wind speed forecasting model based on multi-objective algorithm and interpretability learning," Energy, Elsevier, vol. 269(C).
  3. Wang, Deyun & Luo, Hongyuan & Grunder, Olivier & Lin, Yanbing, 2017. "Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction," Renewable Energy, Elsevier, vol. 113(C), pages 1345-1358.
  4. 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).
  5. Reihani, Ehsan & Motalleb, Mahdi & Ghorbani, Reza & Saad Saoud, Lyes, 2016. "Load peak shaving and power smoothing of a distribution grid with high renewable energy penetration," Renewable Energy, Elsevier, vol. 86(C), pages 1372-1379.
  6. Tonglin Fu & Xinrong Li, 2020. "A Combination Forecasting Strategy for Precipitation, Temperature and Wind Speed in the Southeastern Margin of the Tengger Desert," Sustainability, MDPI, vol. 12(4), pages 1-22, February.
  7. Fang, Ping & Fu, Wenlong & Wang, Kai & Xiong, Dongzhen & Zhang, Kai, 2022. "A compositive architecture coupling outlier correction, EWT, nonlinear Volterra multi-model fusion with multi-objective optimization for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 307(C).
  8. Paweł Pijarski & Piotr Kacejko & Piotr Miller, 2023. "Advanced Optimisation and Forecasting Methods in Power Engineering—Introduction to the Special Issue," Energies, MDPI, vol. 16(6), pages 1-20, March.
  9. Chinmoy, Lakshmi & Iniyan, S. & Goic, Ranko, 2019. "Modeling wind power investments, policies and social benefits for deregulated electricity market – A review," Applied Energy, Elsevier, vol. 242(C), pages 364-377.
  10. 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.
  11. Liu, Xingdou & Zhang, Li & Wang, Jiangong & Zhou, Yue & Gan, Wei, 2023. "A unified multi-step wind speed forecasting framework based on numerical weather prediction grids and wind farm monitoring data," Renewable Energy, Elsevier, vol. 211(C), pages 948-963.
  12. Tansu Filik, 2016. "Improved Spatio-Temporal Linear Models for Very Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 9(3), pages 1-15, March.
  13. Fu, Wenlong & Fang, Ping & Wang, Kai & Li, Zhenxing & Xiong, Dongzhen & Zhang, Kai, 2021. "Multi-step ahead short-term wind speed forecasting approach coupling variational mode decomposition, improved beetle antennae search algorithm-based synchronous optimization and Volterra series model," Renewable Energy, Elsevier, vol. 179(C), pages 1122-1139.
  14. 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.
  15. Xin Zhao & Haikun Wei & Chenxi Li & Kanjian Zhang, 2020. "A Hybrid Nonlinear Forecasting Strategy for Short-Term Wind Speed," Energies, MDPI, vol. 13(7), pages 1-15, April.
  16. Wang, Jianzhou & Wang, Shuai & Li, Zhiwu, 2021. "Wind speed deterministic forecasting and probabilistic interval forecasting approach based on deep learning, modified tunicate swarm algorithm, and quantile regression," Renewable Energy, Elsevier, vol. 179(C), pages 1246-1261.
  17. Sandra Minerva Valdivia-Bautista & José Antonio Domínguez-Navarro & Marco Pérez-Cisneros & Carlos Jesahel Vega-Gómez & Beatriz Castillo-Téllez, 2023. "Artificial Intelligence in Wind Speed Forecasting: A Review," Energies, MDPI, vol. 16(5), pages 1-28, March.
  18. Ariyarathne, Sakitha & Gangammanavar, Harsha & Sundararajan, Raanju R., 2022. "Change point detection-based simulation of nonstationary sub-hourly wind time series," Applied Energy, Elsevier, vol. 310(C).
  19. 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.
  20. Huang, Xiaojia & Wang, Chen & Zhang, Shenghui, 2024. "Research and application of a Model selection forecasting system for wind speed and theoretical power generation in wind farms based on classification and wind conversion," Energy, Elsevier, vol. 293(C).
  21. Sepasi, Saeed & Reihani, Ehsan & Howlader, Abdul M. & Roose, Leon R. & Matsuura, Marc M., 2017. "Very short term load forecasting of a distribution system with high PV penetration," Renewable Energy, Elsevier, vol. 106(C), pages 142-148.
  22. 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.
  23. 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.
  24. Wang, Han & Han, Shuang & Liu, Yongqian & Yan, Jie & Li, Li, 2019. "Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system," Applied Energy, Elsevier, vol. 237(C), pages 1-10.
  25. 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.
  26. 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).
  27. Chaoyang Chen & Hualing Liu & Yong Xiao & Fagen Zhu & Li Ding & Fuwen Yang, 2022. "Power Generation Scheduling for a Hydro-Wind-Solar Hybrid System: A Systematic Survey and Prospect," Energies, MDPI, vol. 15(22), pages 1-31, November.
  28. Rossi, Francesco & Velázquez, David, 2015. "A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant," Energy, Elsevier, vol. 89(C), pages 528-544.
  29. Kui Yang & Bofu Wang & Xiang Qiu & Jiahua Li & Yuze Wang & Yulu Liu, 2022. "Multi-Step Short-Term Wind Speed Prediction Models Based on Adaptive Robust Decomposition Coupled with Deep Gated Recurrent Unit," Energies, MDPI, vol. 15(12), pages 1-24, June.
  30. 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).
  31. Akçay, Hüseyin & Filik, Tansu, 2017. "Short-term wind speed forecasting by spectral analysis from long-term observations with missing values," Applied Energy, Elsevier, vol. 191(C), pages 653-662.
  32. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
  33. 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.
  34. Liu, Hui & Tian, Hongqi & Liang, Xifeng & Li, Yanfei, 2015. "New wind speed forecasting approaches using fast ensemble empirical model decomposition, genetic algorithm, Mind Evolutionary Algorithm and Artificial Neural Networks," Renewable Energy, Elsevier, vol. 83(C), pages 1066-1075.
  35. Huan Wang & Jiejun Huang & Han Zhou & Lixue Zhao & Yanbin Yuan, 2019. "An Integrated Variational Mode Decomposition and ARIMA Model to Forecast Air Temperature," Sustainability, MDPI, vol. 11(15), pages 1-11, July.
  36. Yuqi Dong & Xuejiao Ma & Chenchen Ma & Jianzhou Wang, 2016. "Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting," Energies, MDPI, vol. 9(12), pages 1-30, December.
  37. Madeline Hui Li Lee & Yee Chee Ser & Ganeshsree Selvachandran & Pham Huy Thong & Le Cuong & Le Hoang Son & Nguyen Trung Tuan & Vassilis C. Gerogiannis, 2022. "A Comparative Study of Forecasting Electricity Consumption Using Machine Learning Models," Mathematics, MDPI, vol. 10(8), pages 1-23, April.
  38. Zhilong Wang & Chen Wang & Jie Wu, 2016. "Wind Energy Potential Assessment and Forecasting Research Based on the Data Pre-Processing Technique and Swarm Intelligent Optimization Algorithms," Sustainability, MDPI, vol. 8(11), pages 1-32, November.
  39. 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.
  40. Ambach, Daniel & Schmid, Wolfgang, 2015. "Periodic and long range dependent models for high frequency wind speed data," Energy, Elsevier, vol. 82(C), pages 277-293.
  41. 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.
  42. 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.
  43. Li, Wenzhe & Jia, Xiaodong & Li, Xiang & Wang, Yinglu & Lee, Jay, 2021. "A Markov model for short term wind speed prediction by integrating the wind acceleration information," Renewable Energy, Elsevier, vol. 164(C), pages 242-253.
  44. 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.
  45. 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.
  46. 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.
  47. Yuansheng Huang & Shijian Liu & Lei Yang, 2018. "Wind Speed Forecasting Method Using EEMD and the Combination Forecasting Method Based on GPR and LSTM," Sustainability, MDPI, vol. 10(10), pages 1-15, October.
  48. Kameron B. Kinast & Ernest Fokoué, 2024. "An In-Depth Look at Rising Temperatures: Forecasting with Advanced Time Series Models in Major US Regions," Forecasting, MDPI, vol. 6(3), pages 1-24, September.
  49. Wang, Shuai & Wang, Jianzhou & Lu, Haiyan & Zhao, Weigang, 2021. "A novel combined model for wind speed prediction – Combination of linear model, shallow neural networks, and deep learning approaches," Energy, Elsevier, vol. 234(C).
  50. 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.
  51. Chi, Lixun & Su, Huai & Zio, Enrico & Qadrdan, Meysam & Zhou, Jing & Zhang, Li & Fan, Lin & Yang, Zhaoming & Xie, Fei & Zuo, Lili & Zhang, Jinjun, 2023. "A systematic framework for the assessment of the reliability of energy supply in Integrated Energy Systems based on a quasi-steady-state model," Energy, Elsevier, vol. 263(PB).
  52. Qunli Wu & Chenyang Peng, 2015. "Wind Power Grid Connected Capacity Prediction Using LSSVM Optimized by the Bat Algorithm," Energies, MDPI, vol. 8(12), pages 1-15, December.
  53. Liu, Zhenkun & Jiang, Ping & Zhang, Lifang & Niu, Xinsong, 2020. "A combined forecasting model for time series: Application to short-term wind speed forecasting," Applied Energy, Elsevier, vol. 259(C).
  54. Sun, Shaolong & Qiao, Han & Wei, Yunjie & Wang, Shouyang, 2017. "A new dynamic integrated approach for wind speed forecasting," Applied Energy, Elsevier, vol. 197(C), pages 151-162.
  55. Yukun Wang & Aiying Zhao & Xiaoxue Wei & Ranran Li, 2023. "A Novel Ensemble Model Based on an Advanced Optimization Algorithm for Wind Speed Forecasting," Energies, MDPI, vol. 16(14), pages 1-19, July.
  56. Xue Zhou & Yajian Ke & Jianhui Zhu & Weiwei Cui, 2023. "Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting," Sustainability, MDPI, vol. 16(1), pages 1-26, December.
  57. Zhou, Yifan & Miao, Jindan & Yan, Bin & Zhang, Zhisheng, 2020. "Bio-objective long-term maintenance scheduling for wind turbines in multiple wind farms," Renewable Energy, Elsevier, vol. 160(C), pages 1136-1147.
  58. 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.
  59. 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.
  60. Lilin Cheng & Haixiang Zang & Tao Ding & Rong Sun & Miaomiao Wang & Zhinong Wei & Guoqiang Sun, 2018. "Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach," Energies, MDPI, vol. 11(8), pages 1-23, July.
  61. Viktor Elistratov & Mikhail Konishchev & Roman Denisov & Inna Bogun & Aki Grönman & Teemu Turunen-Saaresti & Afonso Julian Lugo, 2021. "Study of the Intelligent Control and Modes of the Arctic-Adopted Wind–Diesel Hybrid System," Energies, MDPI, vol. 14(14), pages 1-14, July.
  62. 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.
  63. Zhang, Yagang & Zhang, Jinghui & Yu, Leyi & Pan, Zhiya & Feng, Changyou & Sun, Yiqian & Wang, Fei, 2022. "A short-term wind energy hybrid optimal prediction system with denoising and novel error correction technique," Energy, Elsevier, vol. 254(PC).
  64. López, Germánico & Arboleya, Pablo, 2022. "Short-term wind speed forecasting over complex terrain using linear regression models and multivariable LSTM and NARX networks in the Andes Mountains, Ecuador," Renewable Energy, Elsevier, vol. 183(C), pages 351-368.
  65. Ping Wang & Xuran He & Hongyinping Feng & Guisheng Zhang & Chenglu Rong, 2021. "A Hybrid Model for PM 2.5 Concentration Forecasting Based on Neighbor Structural Information, a Case in North China," Sustainability, MDPI, vol. 13(2), pages 1-19, January.
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