Stochastic financial appraisal of offshore wind farms
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DOI: 10.1016/j.renene.2019.06.111
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Cited by:
- Wu, Yunna & Liu, Fangtong & Wu, Junhao & He, Jiaming & Xu, Minjia & Zhou, Jianli, 2022. "Barrier identification and analysis framework to the development of offshore wind-to-hydrogen projects," Energy, Elsevier, vol. 239(PB).
- Sara C. Pryor & Rebecca J. Barthelmie, 2024. "Power Production, Inter- and Intra-Array Wake Losses from the U.S. East Coast Offshore Wind Energy Lease Areas," Energies, MDPI, vol. 17(5), pages 1-30, February.
- Rinaldi, Giovanni & Garcia-Teruel, Anna & Jeffrey, Henry & Thies, Philipp R. & Johanning, Lars, 2021. "Incorporating stochastic operation and maintenance models into the techno-economic analysis of floating offshore wind farms," Applied Energy, Elsevier, vol. 301(C).
- Mingyu Li & Dongxiao Niu & Zhengsen Ji & Xiwen Cui & Lijie Sun, 2021. "Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net," Sustainability, MDPI, vol. 13(21), pages 1-19, November.
- Tvedt, Jostein, 2022. "Floating offshore wind and the real options to relocate," Energy Economics, Elsevier, vol. 116(C).
- Bórawski, Piotr & Bełdycka-Bórawska, Aneta & Jankowski, Krzysztof Jóżef & Dubis, Bogdan & Dunn, James W., 2020. "Development of wind energy market in the European Union," Renewable Energy, Elsevier, vol. 161(C), pages 691-700.
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Keywords
Offshore wind; Stochastic financial appraisal; ARIMA; Artificial neural networks; Monte Carlo simulation;All these keywords.
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