Open Access
Issue |
E3S Web Conf.
Volume 354, 2022
International Energy2021-Conference on “Renewable Energy and Digital Technologies for the Development of Africa”
|
|
---|---|---|
Article Number | 02004 | |
Number of page(s) | 6 | |
Section | Sustainable Electricity Systems and Applications | |
DOI | https://doi.org/10.1051/e3sconf/202235402004 | |
Published online | 13 July 2022 |
- Africa. Energy. Series, “Kenya Special report 2020,” (2020). [Google Scholar]
- P. F. Achieng, B. Davidsdottir, and I. Birgir, “Potential contribution of geothermal energy to climate change adaptation : A case study of the arid and semi-arid eastern Baringo lowlands , Kenya ଝ,” Renewable and Sustainable Energy Reviews, vol. 16, no. 6, pp. 4222–4246, 2012, doi: 10.1016/j.rser.2012.01.081. [CrossRef] [Google Scholar]
- D. Samoita, C. Nzila, and P. A. Østergaard, “Barriers and Solutions for Increasing the Integration of Solar Photovoltaic in Kenya ’ s Electricity Mix,” (2020). [Google Scholar]
- Solargis, “Kenya_PVOUT_mid-size-map_156x220mm-300dpi_v20191015.” 2019. [Google Scholar]
- A.M.K. El-Ghonemy, “Photovoltaic Solar Energy : Review,” International Journal of Scientific & Engineering Research , vol. 3, no. 11, pp. 1–43, 2012, [Online]. Available: https://www.ijser.org/researchpaper/Phot [Google Scholar]
- F. Touati, M. A. Al-Hitmi, N. A. Chowdhury, J. A. Hamad, and A. J. R. San Pedro Gonzales, “Investigation of solar PV performance under Doha weather using a customized measurement and monitoring system,” Renewable Energy , vol. 89, pp. 564–577, (2016), doi: 10.1016/j.renene.2015.12.046. [CrossRef] [Google Scholar]
- A. Khandakar et al., “Machine Learning Based Photovoltaics ( PV ) Power Prediction Using Di ff erent Environmental Parameters of Qatar,” (2019). [Google Scholar]
- M. G. De Giorgi, P. M. Congedo, and M. Malvoni, “Photovoltaic power forecasting using statistical methods : impact of weather data,” no. September (2015), doi: 10.1049/iet-smt.2013.0135. [Google Scholar]
- C. Wu and Y. Lou, “Predicting solar generation from weather forecasts,” pp. 528–533, (2011). [Google Scholar]
- M. Madhiarasan and S. N. Deepa, “Review of Forecasters Application to Solar Irradiance Forecasting,” vol. 2, no. 2, pp. 26–30, (2017). [Google Scholar]
- A. J. Smola and B. S. C. H. Olkopf, “A tutorial on support vector regression ∗ ¨,” pp. 199–222, (2004). [Google Scholar]
- L. Breiman, “Random Forests,” pp. 1–33, (2001). [Google Scholar]
- S.-G. Kim, J.-Y. Jung, and M. Sim, “A Two-Step Approach to Solar Power Generation Prediction Based on Weather Data Using Machine Learning,” Sustainability, vol. 11, no. 5, p. 1501, (2019), doi: 10.3390/su11051501. [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.