IDEAS home Printed from https://ideas.repec.org/r/eee/renene/v135y2019icp674-686.html

Wind power forecasting for a real onshore wind farm on complex terrain using WRF high resolution simulations

Citations

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


Cited by:

  1. Cao, Yuzhe & Huang, Xuefei & Liu, Jing & Cai, Defu & Ding, Yuemin & Lu, Renzhi, 2025. "DDRGS2S: A novel spatiotemporal correlation-based deep learning model for wind power prediction," Energy, Elsevier, vol. 338(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. Li, Jinghua & Zhou, Jiasheng & Chen, Bo, 2020. "Review of wind power scenario generation methods for optimal operation of renewable energy systems," Applied Energy, Elsevier, vol. 280(C).
  4. González-Alonso de Linaje, N. & Mattar, C. & Borvarán, D., 2019. "Quantifying the wind energy potential differences using different WRF initial conditions on Mediterranean coast of Chile," Energy, Elsevier, vol. 188(C).
  5. D’Isidoro, Massimo & Briganti, Gino & Vitali, Lina & Righini, Gaia & Adani, Mario & Guarnieri, Guido & Moretti, Lorenzo & Raliselo, Muso & Mahahabisa, Mabafokeng & Ciancarella, Luisella & Zanini, Gabr, 2020. "Estimation of solar and wind energy resources over Lesotho and their complementarity by means of WRF yearly simulation at high resolution," Renewable Energy, Elsevier, vol. 158(C), pages 114-129.
  6. Wang, Yong & Yang, Zhongsen & Zhou, Ying & Liu, Hao & Yang, Rui & Sun, Lang & Sapnken, Flavian Emmanuel & Narayanan, Govindasami, 2025. "A novel structure adaptive new information priority grey Bernoulli model and its application in China's renewable energy production," Renewable Energy, Elsevier, vol. 239(C).
  7. Lu, Hongfang & Ma, Xin & Huang, Kun & Azimi, Mohammadamin, 2020. "Prediction of offshore wind farm power using a novel two-stage model combining kernel-based nonlinear extension of the Arps decline model with a multi-objective grey wolf optimizer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
  8. Jin, Jingxin & Li, Yilin & Ye, Lin & Xu, Xunjian & Lu, Jiazheng, 2023. "Integration of atmospheric stability in wind resource assessment through multi-scale coupling method," Applied Energy, Elsevier, vol. 348(C).
  9. Qiao, Dalei & Wu, Shun & Li, Ge & You, Jiaxing & Zhang, Juan & Shen, Bilong, 2022. "Wind speed forecasting using multi-site collaborative deep learning for complex terrain application in valleys," Renewable Energy, Elsevier, vol. 189(C), pages 231-244.
  10. Liu, Hui & Duan, Zhu & Chen, Chao, 2020. "Wind speed big data forecasting using time-variant multi-resolution ensemble model with clustering auto-encoder," Applied Energy, Elsevier, vol. 280(C).
  11. Francesco Pasanisi & Gaia Righini & Massimo D’Isidoro & Lina Vitali & Gino Briganti & Sergio Grauso & Lorenzo Moretti & Carlo Tebano & Gabriele Zanini & Mabafokeng Mahahabisa & Mosuoe Letuma & Muso Ra, 2021. "A Cooperation Project in Lesotho: Renewable Energy Potential Maps Embedded in a WebGIS Tool," Sustainability, MDPI, vol. 13(18), pages 1-26, September.
  12. Pedruzzi, Rizzieri & Silva, Allan Rodrigues & Soares dos Santos, Thalyta & Araujo, Allan Cavalcante & Cotta Weyll, Arthur Lúcide & Lago Kitagawa, Yasmin Kaore & Nunes da Silva Ramos, Diogo & Milani de, 2023. "Review of mapping analysis and complementarity between solar and wind energy sources," Energy, Elsevier, vol. 283(C).
  13. Duarte Jacondino, William & Nascimento, Ana Lucia da Silva & Calvetti, Leonardo & Fisch, Gilberto & Augustus Assis Beneti, Cesar & da Paz, Sheila Radman, 2021. "Hourly day-ahead wind power forecasting at two wind farms in northeast Brazil using WRF model," Energy, Elsevier, vol. 230(C).
  14. Annas Fauzy & Cheng-Dar Yue & Chien-Cheng Tu & Ta-Hui Lin, 2021. "Understanding the Potential of Wind Farm Exploitation in Tropical Island Countries: A Case for Indonesia," Energies, MDPI, vol. 14(9), pages 1-26, May.
  15. Chen, Xin & Ye, Xiaoling & Shi, Jian & Zhang, Yingchao & Xiong, Xiong, 2024. "A spatial transfer-based hybrid model for wind speed forecasting," Energy, Elsevier, vol. 313(C).
  16. Xiong, Xiong & Zou, Ruilin & Sheng, Tao & Zeng, Weilin & Ye, Xiaoling, 2023. "An ultra-short-term wind speed correction method based on the fluctuation characteristics of wind speed," Energy, Elsevier, vol. 283(C).
  17. De Moliner, Giorgia & Giani, Paolo & Lonati, Giovanni & Crippa, Paola, 2024. "Sensitivity of multiscale large Eddy simulations for wind power calculations in complex terrain," Applied Energy, Elsevier, vol. 364(C).
  18. Mateusz Rzeszutek & Adriana Kłosowska & Robert Oleniacz, 2023. "Accuracy Assessment of WRF Model in the Context of Air Quality Modeling in Complex Terrain," Sustainability, MDPI, vol. 15(16), pages 1-27, August.
  19. Buen Zhang & Shyuan Cheng & Fanghan Lu & Yuan Zheng & Leonardo P. Chamorro, 2020. "Impact of Topographic Steps in the Wake and Power of a Wind Turbine: Part A—Statistics," Energies, MDPI, vol. 13(23), pages 1-14, December.
  20. Ahmad, Tanveer & Zhang, Dongdong, 2022. "A data-driven deep sequence-to-sequence long-short memory method along with a gated recurrent neural network for wind power forecasting," Energy, Elsevier, vol. 239(PB).
  21. Wu, Chunlei & Luo, Kun & Wang, Qiang & Fan, Jianren, 2022. "Simulated potential wind power sensitivity to the planetary boundary layer parameterizations combined with various topography datasets in the weather research and forecasting model," Energy, Elsevier, vol. 239(PB).
  22. Carlos Otero-Casal & Platon Patlakas & Miguel A. Prósper & George Galanis & Gonzalo Miguez-Macho, 2019. "Development of a High-Resolution Wind Forecast System Based on the WRF Model and a Hybrid Kalman-Bayesian Filter," Energies, MDPI, vol. 12(16), pages 1-19, August.
  23. 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).
  24. Du, Pei & Yang, Dongchuan & Li, Yanzhao & Wang, Jianzhou, 2024. "An innovative interpretable combined learning model for wind speed forecasting," Applied Energy, Elsevier, vol. 358(C).
  25. Li, Jiale & Song, Zihao & Wang, Xuefei & Wang, Yanru & Jia, Yaya, 2022. "A novel offshore wind farm typhoon wind speed prediction model based on PSO–Bi-LSTM improved by VMD," Energy, Elsevier, vol. 251(C).
  26. Martyna Kubiak & Artur Bugała & Dorota Bugała & Wojciech Czekała, 2025. "Simulation Analysis of Onshore and Offshore Wind Farms’ Generation Potential for Polish Climatic Conditions," Energies, MDPI, vol. 18(15), pages 1-42, August.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.