Wind power forecasting for a real onshore wind farm on complex terrain using WRF high resolution simulations
Author
Abstract
Suggested Citation
DOI: 10.1016/j.renene.2018.12.047
Download full text from publisher
As the access to this document is restricted, you may want to
for a different version of it.References listed on IDEAS
- Robert Vautard & Françoise Thais & Isabelle Tobin & François-Marie Bréon & Jean-Guy Devezeaux de Lavergne & Augustin Colette & Pascal Yiou & Paolo Michele Ruti, 2014. "Regional climate model simulations indicate limited climatic impacts by operational and planned European wind farms," Nature Communications, Nature, vol. 5(1), pages 1-9, May.
- Zhao, Jing & Guo, Zhen-Hai & Su, Zhong-Yue & Zhao, Zhi-Yuan & Xiao, Xia & Liu, Feng, 2016. "An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed," Applied Energy, Elsevier, vol. 162(C), pages 808-826.
- Santos, J.A. & Rochinha, C. & Liberato, M.L.R. & Reyers, M. & Pinto, J.G., 2015. "Projected changes in wind energy potentials over Iberia," Renewable Energy, Elsevier, vol. 75(C), pages 68-80.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
- 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.
- Wasilewski, J. & Baczynski, D., 2017. "Short-term electric energy production forecasting at wind power plants in pareto-optimality context," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 177-187.
- Niu, Tong & Wang, Jianzhou & Zhang, Kequan & Du, Pei, 2018. "Multi-step-ahead wind speed forecasting based on optimal feature selection and a modified bat algorithm with the cognition strategy," Renewable Energy, Elsevier, vol. 118(C), pages 213-229.
- Shakeri, Mohammad & Shayestegan, Mohsen & Reza, S.M. Salim & Yahya, Iskandar & Bais, Badariah & Akhtaruzzaman, Md & Sopian, Kamaruzzaman & Amin, Nowshad, 2018. "Implementation of a novel home energy management system (HEMS) architecture with solar photovoltaic system as supplementary source," Renewable Energy, Elsevier, vol. 125(C), pages 108-120.
- Chen, Xue-Jun & Zhao, Jing & Jia, Xiao-Zhong & Li, Zhong-Long, 2021. "Multi-step wind speed forecast based on sample clustering and an optimized hybrid system," Renewable Energy, Elsevier, vol. 165(P1), pages 595-611.
- 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.
- Yang, Zhongshan & Wang, Jian, 2018. "A hybrid forecasting approach applied in wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Energy, Elsevier, vol. 160(C), pages 87-100.
- Jerez, S. & Thais, F. & Tobin, I. & Wild, M. & Colette, A. & Yiou, P. & Vautard, R., 2015. "The CLIMIX model: A tool to create and evaluate spatially-resolved scenarios of photovoltaic and wind power development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 1-15.
- Hu, Yue & Liu, Hanjing & Wu, Senzhen & Zhao, Yuan & Wang, Zhijin & Liu, Xiufeng, 2024. "Temporal collaborative attention for wind power forecasting," Applied Energy, Elsevier, vol. 357(C).
- 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.
- Ping Jiang & Zeng Wang & Kequan Zhang & Wendong Yang, 2017. "An Innovative Hybrid Model Based on Data Pre-Processing and Modified Optimization Algorithm and Its Application in Wind Speed Forecasting," Energies, MDPI, vol. 10(7), pages 1-29, July.
- Yang, Wendong & Wang, Jianzhou & Niu, Tong & Du, Pei, 2019. "A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting," Applied Energy, Elsevier, vol. 235(C), pages 1205-1225.
- Wang, Qin & Wu, Hongyu & Florita, Anthony R. & Brancucci Martinez-Anido, Carlo & Hodge, Bri-Mathias, 2016. "The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales," Applied Energy, Elsevier, vol. 184(C), pages 696-713.
- Tian, Chengshi & Hao, Yan & Hu, Jianming, 2018. "A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization," Applied Energy, Elsevier, vol. 231(C), pages 301-319.
- Liu, Chenyu & Zhang, Xuemin & Mei, Shengwei & Zhen, Zhao & Jia, Mengshuo & Li, Zheng & Tang, Haiyan, 2022. "Numerical weather prediction enhanced wind power forecasting: Rank ensemble and probabilistic fluctuation awareness," Applied Energy, Elsevier, vol. 313(C).
- 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.
- Li, Dan & Jiang, Fuxin & Chen, Min & Qian, Tao, 2022. "Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks," Energy, Elsevier, vol. 238(PC).
- Tian, Chaonan & Niu, Tong & Wei, Wei, 2022. "Developing a wind power forecasting system based on deep learning with attention mechanism," Energy, Elsevier, vol. 257(C).
- Yuan, Renyu & Ji, Wenju & Luo, Kun & Wang, Jianwen & Zhang, Sanxia & Wang, Qiang & Fan, Jianren & Ni, MingJiang & Cen, Kefa, 2017. "Coupled wind farm parameterization with a mesoscale model for simulations of an onshore wind farm," Applied Energy, Elsevier, vol. 206(C), pages 113-125.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:135:y:2019:i:c:p:674-686. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.
Printed from https://ideas.repec.org/a/eee/renene/v135y2019icp674-686.html