IDEAS home Printed from https://ideas.repec.org/r/eee/appene/v224y2018icp550-566.html
   My bibliography  Save this item

Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Alipour, Mohammadali & Aghaei, Jamshid & Norouzi, Mohammadali & Niknam, Taher & Hashemi, Sattar & Lehtonen, Matti, 2020. "A novel electrical net-load forecasting model based on deep neural networks and wavelet transform integration," Energy, Elsevier, vol. 205(C).
  2. Zhao, Jing & Guo, Zhenhai & Guo, Yanling & Lin, Wantao & Zhu, Wenjin, 2021. "A self-organizing forecast of day-ahead wind speed: Selective ensemble strategy based on numerical weather predictions," Energy, Elsevier, vol. 218(C).
  3. Seyed Ali Kashani & Alireza Soleimani & Ali Khosravi & Mojtaba Mirsalim, 2022. "State-of-the-Art Research on Wireless Charging of Electric Vehicles Using Solar Energy," Energies, MDPI, vol. 16(1), pages 1-27, December.
  4. Behnam Talebjedi & Ali Khosravi & Timo Laukkanen & Henrik Holmberg & Esa Vakkilainen & Sanna Syri, 2020. "Energy Modeling of a Refiner in Thermo-Mechanical Pulping Process Using ANFIS Method," Energies, MDPI, vol. 13(19), pages 1-26, October.
  5. Zhiyong Guo & Fangzheng Wei & Wenkai Qi & Qiaoli Han & Huiyuan Liu & Xiaomei Feng & Minghui Zhang, 2024. "A Time Series Prediction Model for Wind Power Based on the Empirical Mode Decomposition–Convolutional Neural Network–Three-Dimensional Gated Neural Network," Sustainability, MDPI, vol. 16(8), pages 1-20, April.
  6. 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).
  7. Tahmasebifar, Reza & Moghaddam, Mohsen Parsa & Sheikh-El-Eslami, Mohammad Kazem & Kheirollahi, Reza, 2020. "A new hybrid model for point and probabilistic forecasting of wind power," Energy, Elsevier, vol. 211(C).
  8. Anfeng Zhu & Qiancheng Zhao & Xian Wang & Ling Zhou, 2022. "Ultra-Short-Term Wind Power Combined Prediction Based on Complementary Ensemble Empirical Mode Decomposition, Whale Optimisation Algorithm, and Elman Network," Energies, MDPI, vol. 15(9), pages 1-17, April.
  9. Bilal, Boudy & Adjallah, Kondo Hloindo & Sava, Alexandre & Yetilmezsoy, Kaan & Ouassaid, Mohammed, 2023. "Wind turbine output power prediction and optimization based on a novel adaptive neuro-fuzzy inference system with the moving window," Energy, Elsevier, vol. 263(PE).
  10. Zang, Haixiang & Cheng, Lilin & Ding, Tao & Cheung, Kwok W. & Wang, Miaomiao & Wei, Zhinong & Sun, Guoqiang, 2019. "Estimation and validation of daily global solar radiation by day of the year-based models for different climates in China," Renewable Energy, Elsevier, vol. 135(C), pages 984-1003.
  11. 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.
  12. Bilal, Boudy & Adjallah, Kondo Hloindo & Sava, Alexandre & Yetilmezsoy, Kaan & Kıyan, Emel, 2022. "Wind power conversion system model identification using adaptive neuro-fuzzy inference systems: A case study," Energy, Elsevier, vol. 239(PB).
  13. Zhao, Haitao & Ezeh, Collins I. & Ren, Weijia & Li, Wentao & Pang, Cheng Heng & Zheng, Chenghang & Gao, Xiang & Wu, Tao, 2019. "Integration of machine learning approaches for accelerated discovery of transition-metal dichalcogenides as Hg0 sensing materials," Applied Energy, Elsevier, vol. 254(C).
  14. Paweł Piotrowski & Inajara Rutyna & Dariusz Baczyński & Marcin Kopyt, 2022. "Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors," Energies, MDPI, vol. 15(24), pages 1-38, December.
  15. Won Joong Kim & Gunho Jung & Sun-Yong Choi, 2020. "Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning," Complexity, Hindawi, vol. 2020, pages 1-23, July.
  16. Marcin Blachnik & Sławomir Walkowiak & Adam Kula, 2023. "Large Scale, Mid Term Wind Farms Power Generation Prediction," Energies, MDPI, vol. 16(5), pages 1-15, March.
  17. Ana Lagos & Joaquín E. Caicedo & Gustavo Coria & Andrés Romero Quete & Maximiliano Martínez & Gastón Suvire & Jesús Riquelme, 2022. "State-of-the-Art Using Bibliometric Analysis of Wind-Speed and -Power Forecasting Methods Applied in Power Systems," Energies, MDPI, vol. 15(18), pages 1-40, September.
  18. Khosravi, Ali & Malekan, Mohammad & Assad, Mamdouh E.H., 2019. "Numerical analysis of magnetic field effects on the heat transfer enhancement in ferrofluids for a parabolic trough solar collector," Renewable Energy, Elsevier, vol. 134(C), pages 54-63.
  19. Chen, Hao & Zhang, Chao & Yu, Haizeng & Wang, Zhilin & Duncan, Ian & Zhou, Xianmin & Liu, Xiliang & Wang, Yu & Yang, Shenglai, 2022. "Application of machine learning to evaluating and remediating models for energy and environmental engineering," Applied Energy, Elsevier, vol. 320(C).
  20. Noman Khan & Fath U Min Ullah & Ijaz Ul Haq & Samee Ullah Khan & Mi Young Lee & Sung Wook Baik, 2021. "AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting," Mathematics, MDPI, vol. 9(19), pages 1-18, October.
  21. Hong, Ying-Yi & Rioflorido, Christian Lian Paulo P., 2019. "A hybrid deep learning-based neural network for 24-h ahead wind power forecasting," Applied Energy, Elsevier, vol. 250(C), pages 530-539.
  22. Yu, Ruiguo & Liu, Zhiqiang & Li, Xuewei & Lu, Wenhuan & Ma, Degang & Yu, Mei & Wang, Jianrong & Li, Bin, 2019. "Scene learning: Deep convolutional networks for wind power prediction by embedding turbines into grid space," Applied Energy, Elsevier, vol. 238(C), pages 249-257.
  23. Dong, Zhen & Li, Zhongguo & Liang, Zhongchao & Xu, Yiqiao & Ding, Zhengtao, 2021. "Distributed neural network enhanced power generation strategy of large-scale wind power plant for power expansion," Applied Energy, Elsevier, vol. 303(C).
  24. Qin, Li & Liu, Shi & Kang, Yi & Yan, Song An & Inaki Schlaberg, H. & Wang, Zhan, 2019. "Wind velocity distribution reconstruction using CFD database with Tucker decomposition and sensor measurement," Energy, Elsevier, vol. 167(C), pages 1236-1250.
  25. Xiaohan Xu & Roy Anthony Rogers & Mario Arturo Ruiz Estrada, 2023. "A Novel Prediction Model: ELM-ABC for Annual GDP in the Case of SCO Countries," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1545-1566, December.
  26. Liu, Hui & Duan, Zhu & Li, Yanfei & Lu, Haibo, 2018. "A novel ensemble model of different mother wavelets for wind speed multi-step forecasting," Applied Energy, Elsevier, vol. 228(C), pages 1783-1800.
  27. Li, Lei & Yin, Xiao-Li & Jia, Xin-Chun & Sobhani, Behrooz, 2020. "Day ahead powerful probabilistic wind power forecast using combined intelligent structure and fuzzy clustering algorithm," Energy, Elsevier, vol. 192(C).
  28. Shamsi, Meisam & Babazadeh, Reza, 2022. "Estimation and prediction of Jatropha cultivation areas in China and India," Renewable Energy, Elsevier, vol. 183(C), pages 548-560.
  29. Imre Delgado & Muhammad Fahim, 2020. "Wind Turbine Data Analysis and LSTM-Based Prediction in SCADA System," Energies, MDPI, vol. 14(1), pages 1-21, December.
  30. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
  31. Yang, Rui & Liu, Hui & Nikitas, Nikolaos & Duan, Zhu & Li, Yanfei & Li, Ye, 2022. "Short-term wind speed forecasting using deep reinforcement learning with improved multiple error correction approach," Energy, Elsevier, vol. 239(PB).
  32. Jianzhou Wang & Chunying Wu & Tong Niu, 2019. "A Novel System for Wind Speed Forecasting Based on Multi-Objective Optimization and Echo State Network," Sustainability, MDPI, vol. 11(2), pages 1-34, January.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.