E3S Web Conf.
Volume 354, 2022International Energy2021-Conference on “Renewable Energy and Digital Technologies for the Development of Africa”
|Number of page(s)||6|
|Section||Sustainable Electricity Systems and Applications|
|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]
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