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Influence of local wind speed and direction on wind power dynamics – Application to offshore very short-term forecasting

Citations

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Cited by:

  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. Yitian Xing & Fue-Sang Lien & William Melek & Eugene Yee, 2022. "A Multi-Hour Ahead Wind Power Forecasting System Based on a WRF-TOPSIS-ANFIS Model," Energies, MDPI, vol. 15(15), pages 1-35, July.
  3. 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.
  4. Luigi Spezia & Andy Vinten & Roberta Paroli & Marc Stutter, 2021. "An evolutionary Monte Carlo method for the analysis of turbidity high‐frequency time series through Markov switching autoregressive models," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
  5. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2014. "WRF wind simulation and wind energy production estimates forced by different reanalyses: Comparison with observed data for Portugal," Applied Energy, Elsevier, vol. 117(C), pages 116-126.
  6. Liu, Hui & Chen, Chao, 2019. "Multi-objective data-ensemble wind speed forecasting model with stacked sparse autoencoder and adaptive decomposition-based error correction," Applied Energy, Elsevier, vol. 254(C).
  7. Campos, R.M. & Guedes Soares, C., 2018. "Spatial distribution of offshore wind statistics on the coast of Portugal using Regional Frequency Analysis," Renewable Energy, Elsevier, vol. 123(C), pages 806-816.
  8. Gallego-Castillo, Cristobal & Bessa, Ricardo & Cavalcante, Laura & Lopez-Garcia, Oscar, 2016. "On-line quantile regression in the RKHS (Reproducing Kernel Hilbert Space) for operational probabilistic forecasting of wind power," Energy, Elsevier, vol. 113(C), pages 355-365.
  9. Han, Shuang & Qiao, Yan-hui & Yan, Jie & Liu, Yong-qian & Li, Li & Wang, Zheng, 2019. "Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network," Applied Energy, Elsevier, vol. 239(C), pages 181-191.
  10. Burlibaşa, A. & Ceangă, E., 2013. "Rotationally sampled spectrum approach for simulation of wind speed turbulence in large wind turbines," Applied Energy, Elsevier, vol. 111(C), pages 624-635.
  11. G. Ponkumar & S. Jayaprakash & Karthick Kanagarathinam, 2023. "Advanced Machine Learning Techniques for Accurate Very-Short-Term Wind Power Forecasting in Wind Energy Systems Using Historical Data Analysis," Energies, MDPI, vol. 16(14), pages 1-24, July.
  12. Herbert Amezquita & Pedro M. S. Carvalho & Hugo Morais, 2023. "Wind Forecast at Medium Voltage Distribution Networks," Energies, MDPI, vol. 16(6), pages 1-23, March.
  13. Wang, Yun & Hu, Qinghua & Meng, Deyu & Zhu, Pengfei, 2017. "Deterministic and probabilistic wind power forecasting using a variational Bayesian-based adaptive robust multi-kernel regression model," Applied Energy, Elsevier, vol. 208(C), pages 1097-1112.
  14. Saira Al-Zadjali & Ahmed Al Maashri & Amer Al-Hinai & Rashid Al Abri & Swaroop Gajare & Sultan Al Yahyai & Mostafa Bakhtvar, 2021. "A Fast and Accurate Wind Speed and Direction Nowcasting Model for Renewable Energy Management Systems," Energies, MDPI, vol. 14(23), pages 1-20, November.
  15. Pierre-Julien Trombe & Pierre Pinson & Henrik Madsen, 2012. "A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations," Energies, MDPI, vol. 5(3), pages 1-37, March.
  16. Liu, Hui & Chen, Chao, 2019. "Data processing strategies in wind energy forecasting models and applications: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 392-408.
  17. Schweizer, Joerg & Antonini, Alessandro & Govoni, Laura & Gottardi, Guido & Archetti, Renata & Supino, Enrico & Berretta, Claudia & Casadei, Carlo & Ozzi, Claudia, 2016. "Investigating the potential and feasibility of an offshore wind farm in the Northern Adriatic Sea," Applied Energy, Elsevier, vol. 177(C), pages 449-463.
  18. 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.
  19. Yiqi Chu & Chengcai Li & Yefang Wang & Jing Li & Jian Li, 2016. "A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction," Energies, MDPI, vol. 9(11), pages 1-20, October.
  20. Shahram Hanifi & Xiaolei Liu & Zi Lin & Saeid Lotfian, 2020. "A Critical Review of Wind Power Forecasting Methods—Past, Present and Future," Energies, MDPI, vol. 13(15), pages 1-24, July.
  21. Jung, Jaesung & Tam, Kwa-Sur, 2013. "A frequency domain approach to characterize and analyze wind speed patterns," Applied Energy, Elsevier, vol. 103(C), pages 435-443.
  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. Abdoos, Ali Akbar & Abdoos, Hatef & Kazemitabar, Javad & Mobashsher, Mohammad Mehdi & Khaloo, Hooman, 2023. "An intelligent hybrid method based on Monte Carlo simulation for short-term probabilistic wind power prediction," Energy, Elsevier, vol. 278(PA).
  24. Xiao, Yulong & Zou, Chongzhe & Chi, Hetian & Fang, Rengcun, 2023. "Boosted GRU model for short-term forecasting of wind power with feature-weighted principal component analysis," Energy, Elsevier, vol. 267(C).
  25. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2014. "Sensitivity of the WRF model wind simulation and wind energy production estimates to planetary boundary layer parameterizations for onshore and offshore areas in the Iberian Peninsula," Applied Energy, Elsevier, vol. 135(C), pages 234-246.
  26. Zhou, Qingguo & Wang, Chen & Zhang, Gaofeng, 2019. "Hybrid forecasting system based on an optimal model selection strategy for different wind speed forecasting problems," Applied Energy, Elsevier, vol. 250(C), pages 1559-1580.
  27. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2014. "Offshore wind energy resource simulation forced by different reanalyses: Comparison with observed data in the Iberian Peninsula," Applied Energy, Elsevier, vol. 134(C), pages 57-64.
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