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Application of Bayesian model averaging in modeling long-term wind speed distributions

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  1. Li, Gong & Shi, Jing & Qu, Xiuli, 2011. "Modeling methods for GenCo bidding strategy optimization in the liberalized electricity spot market–A state-of-the-art review," Energy, Elsevier, vol. 36(8), pages 4686-4700.
  2. Yang, Tiantian & Chen, Dongwei, 2026. "Beta-generalized Lindley distribution: A novel probability model for wind speed," Renewable Energy, Elsevier, vol. 256(PE).
  3. Raza, Muhammad Qamar & Nadarajah, Mithulananthan & Ekanayake, Chandima, 2017. "Demand forecast of PV integrated bioclimatic buildings using ensemble framework," Applied Energy, Elsevier, vol. 208(C), pages 1626-1638.
  4. Wang, Jianzhou & Hu, Jianming & Ma, Kailiang, 2016. "Wind speed probability distribution estimation and wind energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 881-899.
  5. 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.
  6. Zhou, Wei & O'Neill, Eoghan & Moncaster, Alice & Reiner, David M. & Guthrie, Peter, 2020. "Forecasting urban residential stock turnover dynamics using system dynamics and Bayesian model averaging," Applied Energy, Elsevier, vol. 275(C).
  7. Lepore, Antonio & Palumbo, Biagio & Pievatolo, Antonio, 2020. "A Bayesian approach for site-specific wind rose prediction," Renewable Energy, Elsevier, vol. 150(C), pages 691-702.
  8. Feijóo, Andrés & Villanueva, Daniel, 2016. "Assessing wind speed simulation methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 473-483.
  9. Olgun Aydin & Bartłomiej Igliński & Krzysztof Krukowski & Marek Siemiński, 2022. "Analyzing Wind Energy Potential Using Efficient Global Optimization: A Case Study for the City Gdańsk in Poland," Energies, MDPI, vol. 15(9), pages 1-22, April.
  10. Li, Gong & Shi, Jing & Zhou, Junyi, 2011. "Bayesian adaptive combination of short-term wind speed forecasts from neural network models," Renewable Energy, Elsevier, vol. 36(1), pages 352-359.
  11. Carlos Adrián Hernández-Meléndez & Luis Alberto Rodríguez-Picón & Iván Juan Carlos Pérez-Olguín & Felipe Adrián Vázquez-Galvez & Jesús Israel Hernández-Hernández & Luis Carlos Méndez-González, 2024. "A Site-Specific Wind Energy Potential Analysis Based on Wind Probability Distributions: A Ciudad Juárez-México Case Study," Sustainability, MDPI, vol. 16(21), pages 1-22, October.
  12. Batablinlè, Lamboni & Bazyomo, Serge Dimitri & Badou, Félicien D. & Jean, Hounkpè & Hodabalo, Kamou & Zakari, Djibib & Banna, Magolmeena & Lawin, Agnidé Emmanuel, 2024. "Climate, water, hydropower, wind speed and wind energy potential resources assessments using weather time series data, downscaled regional circulation models: A case study for Mono River Basin in the Gulf of Guinea region," Renewable Energy, Elsevier, vol. 224(C).
  13. Han, Qinkai & Hao, Zhuolin & Hu, Tao & Chu, Fulei, 2018. "Non-parametric models for joint probabilistic distributions of wind speed and direction data," Renewable Energy, Elsevier, vol. 126(C), pages 1032-1042.
  14. Hu, Qinghua & Wang, Yun & Xie, Zongxia & Zhu, Pengfei & Yu, Daren, 2016. "On estimating uncertainty of wind energy with mixture of distributions," Energy, Elsevier, vol. 112(C), pages 935-962.
  15. Adedipe, Tosin & Shafiee, Mahmood & Zio, Enrico, 2020. "Bayesian Network Modelling for the Wind Energy Industry: An Overview," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
  16. Kirchner-Bossi, N. & Prieto, L. & García-Herrera, R. & Carro-Calvo, L. & Salcedo-Sanz, S., 2013. "Multi-decadal variability in a centennial reconstruction of daily wind," Applied Energy, Elsevier, vol. 105(C), pages 30-46.
  17. Allouhi, A. & Zamzoum, O. & Islam, M.R. & Saidur, R. & Kousksou, T. & Jamil, A. & Derouich, A., 2017. "Evaluation of wind energy potential in Morocco's coastal regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 311-324.
  18. Han, Qinkai & Ma, Sai & Wang, Tianyang & Chu, Fulei, 2019. "Kernel density estimation model for wind speed probability distribution with applicability to wind energy assessment in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
  19. Masseran, Nurulkamal, 2015. "Evaluating wind power density models and their statistical properties," Energy, Elsevier, vol. 84(C), pages 533-541.
  20. Wang, Jianzhou & Hu, Jianming & Ma, Kailiang & Zhang, Yixin, 2015. "A self-adaptive hybrid approach for wind speed forecasting," Renewable Energy, Elsevier, vol. 78(C), pages 374-385.
  21. Carro-Calvo, L. & Salcedo-Sanz, S. & Kirchner-Bossi, N. & Portilla-Figueras, A. & Prieto, L. & Garcia-Herrera, R. & Hernández-Martín, E., 2011. "Extraction of synoptic pressure patterns for long-term wind speed estimation in wind farms using evolutionary computing," Energy, Elsevier, vol. 36(3), pages 1571-1581.
  22. Li, Gong & Shi, Jing, 2012. "Applications of Bayesian methods in wind energy conversion systems," Renewable Energy, Elsevier, vol. 43(C), pages 1-8.
  23. Wei Zhou & Eoghan O'Neill & Alice Moncaster & David M Reiner & Peter Guthrie, 2019. "Applying Bayesian Model Averaging to Characterise Urban Residential Stock Turnover Dynamics," Working Papers EPRG1933, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
  24. Douak, Fouzi & Melgani, Farid & Benoudjit, Nabil, 2013. "Kernel ridge regression with active learning for wind speed prediction," Applied Energy, Elsevier, vol. 103(C), pages 328-340.
  25. Kwami Senam A. Sedzro & Adekunlé Akim Salami & Pierre Akuété Agbessi & Mawugno Koffi Kodjo, 2022. "Comparative Study of Wind Energy Potential Estimation Methods for Wind Sites in Togo and Benin (West Sub-Saharan Africa)," Energies, MDPI, vol. 15(22), pages 1-28, November.
  26. Ye, Chengjin & Ding, Yi & Song, Yonghua & Lin, Zhenzhi & Wang, Lei, 2018. "A data driven multi-state model for distribution system flexible planning utilizing hierarchical parallel computing," Applied Energy, Elsevier, vol. 232(C), pages 9-25.
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