Open Access
| Issue |
BIO Web Conf.
Volume 159, 2025
10th International Conference on Sustainable Agriculture, Food, and Energy (SAFE 2024)
|
|
|---|---|---|
| Article Number | 04001 | |
| Number of page(s) | 12 | |
| Section | Sustainable Development Goals (SDGs) | |
| DOI | https://doi.org/10.1051/bioconf/202515904001 | |
| Published online | 05 February 2025 | |
- Shahazad Aziz M Ahmed S Saleem U and Maaz Mufti G, 2017, Wind-hybrid Power Generation Systems Using Renewable Energy Sources-A Review. [Google Scholar]
- Beig A R and Muyeen S M, 2016, Wind energy, in Electric Renewable Energy Systems, (Elsevier), p. 60–77. [Google Scholar]
- Koohi-Fayegh S and Rosen M A, Jul. 2020 A Review of Renewable Energy Options, Applications, Facilitating Technologies and Recent Developments European Journal of Sustainable Development Research 4, 4 p. em0138. [Google Scholar]
- Gupta R A Singh B and Jain B B, 2015 Wind energy conversion system using PMSG in 2015 International Conference on Recent Developments in Control, Automation and Power Engineering (RDCAPE) p. 199–203. [Google Scholar]
- Mohammed D Abdelaziz A S M Mohammed E and Elmostapha E, Oct. 2019 Analysis of wind speed data and wind energy potential using Weibull distribution in Zagora, Morocco International Journal of Renewable Energy Development 8, 3 p. 267–273. [CrossRef] [Google Scholar]
- Ayodele T R Jimoh A A Munda J L and Agee J T, Dec. 2012 Wind distribution and capacity factor estimation for wind turbines in the coastal region of South Africa Energy Convers Manag 64 p. 614–625. [CrossRef] [Google Scholar]
- Jung C and Schindler D, Dec. 2020 The annual cycle and intra-annual variability of the global wind power distribution estimated by the system of wind speed distributions Sustainable Energy Technologies and Assessments 42 p. 100852. [CrossRef] [Google Scholar]
- Millstein D Solomon-Culp J Wang M Ullrich P and Collier C, Apr. 2019 Wind energy variability and links to regional and synoptic scale weather Clim Dyn 52, 7–8 p. 4891–4906. [CrossRef] [Google Scholar]
- Mostafaeipour A Jadidi M Mohammadi K and Sedaghat A, Feb. 2014 An analysis of wind energy potential and economic evaluation in Zahedan, Iran Renewable and Sustainable Energy Reviews 30 p. 641–650. [CrossRef] [Google Scholar]
- Philippopoulos K Deligiorgi D and Karvounis G, Feb. 2012 Wind Speed Distribution Modeling in the Greater Area of Chania, Greece Int J Green Energy 9, 2 p. 174–193. [CrossRef] [Google Scholar]
- Akpinar E K and Akpinar S, Dec. 2004 Statistical analysis of wind energy potential on the basis of the Weibull and Rayleigh distributions for Agin-Elazig, Turkey Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy 218, 8 p. 557–565. [CrossRef] [Google Scholar]
- Hesty N W Cendrawati D G Nepal R and Al Irsyad M I, Nov. 2021 Wind Energy Potential Assessment Based-on WRF Four-Dimensional Data Assimilation System and Cross-Calibrated Multi-Platform Dataset IOP Conf Ser Earth Environ Sci 897, 1 p. 012004. [CrossRef] [Google Scholar]
- Pramuwardani I Hartono Sunarto and Sopaheluwakan A, Nov. 2018 Indonesian rainfall variability during Western North Pacific and Australian monsoon phase related to convectively coupled equatorial waves Arabian Journal of Geosciences 11, 21 p. 673. [CrossRef] [Google Scholar]
- Budi Y Syamsul M Hartrisari H and Chandra W, Sep. 2020 Renewable energy potential for strengthening the energy security in East Sumba–Sumba Island zone International Journal of Energy Production and Management 5, 3 p. 195–211. [CrossRef] [Google Scholar]
- Lomi A, 2016, The Role of Renewable Energy: Sumba Iconic Island, an Implementation of 100 Percent Renewable Energy by 2020, p. 173–184. [Google Scholar]
- Bokde N Feijóo A and Villanueva D, Sep. 2018 Wind Turbine Power Curves Based on the Weibull Cumulative Distribution Function Applied Sciences 8, 10 p. 1757. [CrossRef] [Google Scholar]
- Nayak A K and Mohanty K B, 2018 Analysis of Wind Characteristics using ARMA & Weibull Distribution in 2018 National Power Engineering Conference (NPEC) p. 1–4. [Google Scholar]
- Costa Rocha P A de Sousa R C de Andrade C F and da Silva M E, Jan. 2012 Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil Appl Energy 89, 1 p. 395–400. [CrossRef] [Google Scholar]
- Tonsie Djiela R H Tiam Kapen P and Tchuen G, Aug. 2021 Wind energy of Cameroon by determining Weibull parameters: potential of a environmentally friendly energy International Journal of Environmental Science and Technology 18, 8 p. 2251–2270. [CrossRef] [Google Scholar]
- Kang D Ko K and Huh J, Feb. 2018 Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea Energies (Basel) 11, 2 p. 356. [Google Scholar]
- Pobocikova I and Sedliackova Z, 2014 Comparison of four methods for estimating the Weibull distribution parameters Applied Mathematical Sciences 8 p. 4137–4149. [CrossRef] [Google Scholar]
- Kengne Signe E B Kanmogne A Emmanuel G D and Meva’a L, Nov. 2019 Comparison of seven numerical methods for determining Weibull parameters of wind for sustainable energy in Douala, Cameroon International Journal of Energy Sector Management 13, 4 p. 903–915. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.