Open Access
| Issue |
E3S Web Conf.
Volume 185, 2020
2020 International Conference on Energy, Environment and Bioengineering (ICEEB 2020)
|
|
|---|---|---|
| Article Number | 01079 | |
| Number of page(s) | 5 | |
| Section | Energy Engineering and Power System | |
| DOI | https://doi.org/10.1051/e3sconf/202018501079 | |
| Published online | 01 September 2020 | |
- Yona, A. Senjyu, T. Funabashi, T. (2013) Determination method of insolation prediction with fuzzy and applying neural network for long-term ahead PV power output correction. IEEE Trans. Sustain. Energy, 4: 527–533. [Google Scholar]
- Li, Y. Su, Y. Shu, L. (2014) An ARMAX model for forecasting the power output of a grid connected photovoltaic system. Renewable Energy, 66: 78–89. [Google Scholar]
- Zhang, X. Li, Y. Lu, S. (2019) A solar time based analog ensemble method for regional solar power forecasting, IEEE Trans. Sustain. Energy. 10: 268–279. [Google Scholar]
- Mathiesen, P. Kleissl, J. (2011) Evaluation of numerical weather prediction for intra-day solar forecasting in the continental United States, Solar Energy. 85: 967–977. [Google Scholar]
- Antonanzas, J. Osorio, N. Escobar, R. (2016) Review of photovoltaic power forecasting, Solar Energy. 136: 78–111. [Google Scholar]
- Junior, T. Oozeki, H. Ohtake, T. (2015) Regional forecasts of photovoltaic power generation according to different data availability scenarios: a study of four methods, Progress in Photovoltaics Research & Applications. 23: 1203–1218. [Google Scholar]
- Pierro, M. De Felice, M. Maggioni, E. (2017) Data- driven upscaling methods for regional photovoltaic power estimation and forecast using satellite and numerical weather prediction data, 158: 1026–1038. [Google Scholar]
- Saint-Drenan, Y. M. Good, G. H. Braun, (2016) Analysis of the uncertainty in the estimates of regional PV power generation evaluated with the upscaling method, Solar Energy. 135: 536–550. [CrossRef] [Google Scholar]
- Saint-Drenan, Y. M. Good, G. H. Braun, M. (2017) A probabilistic approach to the estimation of regional photovoltaic power production, Solar Energy. 147: 257–276. [Google Scholar]
- Wang, Z, Li, Q. Wang, G. (2017) Deep learning based ensemble approach for probabilistic wind power forecasting, Applied Energy. 188: 56–70. [Google Scholar]
- Pierro, M. De Felice, M. Maggioni, E. (2017) Data- driven upscaling methods for regional photovoltaic power estimation and forecast using satellite and numerical weather prediction data, Solar Energy. 158: 1026–1038. [CrossRef] [Google Scholar]
- Gu, J. Wang, Z. Kuen, J. (2018) Recent advances in convolutional neural networks’ Pattern Recognition. 77: 354–377. [Google Scholar]
- Hiary, H. Saadeh, H. Saadeh, M. (2018) Flower classification using deep convolutional neural networks, IET Computer Vision. 12: 855–862. [Google Scholar]
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