Open Access
| Issue |
E3S Web of Conf.
Volume 540, 2024
1st International Conference on Power and Energy Systems (ICPES 2023)
|
|
|---|---|---|
| Article Number | 10022 | |
| Number of page(s) | 7 | |
| Section | Grid Connected Systems | |
| DOI | https://doi.org/10.1051/e3sconf/202454010022 | |
| Published online | 21 June 2024 | |
- Inman, R. H., Pedro, H. T., & Coimbra, C. F. (2013). Solar forecasting methods for renewable energy integration. Progress in energy and combustion science, 39(6), 535–576. [CrossRef] [Google Scholar]
- Su, H. I., & El Gamal, A. (2013). Modeling and analysis of the role of energy storage for renewable integration: Power balancing. IEEE Transactions on Power Systems, 28(4), 4109–4117. [CrossRef] [Google Scholar]
- Denholm, P., & Margolis, R. M. (2007). Evaluating the limits of solar photovoltaics (PV) in electric power systems utilizing energy storage and other enabling technologies. Energy Policy, 35(9), 4424–4433. [CrossRef] [Google Scholar]
- Geiger, M., Diabaté, L., Ménard, L., & Wald, L. (2002). A web service for controlling the quality of measurements of global solar irradiation. Solar energy, 73(6), 475–480. [CrossRef] [Google Scholar]
- Mellit, A., & Kalogirou, S. A. (2008). Artificial intelligence techniques for photovoltaic applications: A review. Progress in energy and combustion science, 34(5), 574–632. [CrossRef] [Google Scholar]
- Boyle, G. (Ed.). (2009). Renewable electricity and the grid: the challenge of variability. Routledge. [Google Scholar]
- Martín, L., Zarzalejo, L. F., Polo, J., Navarro, A., Marchante, R., & Cony, M. (2010). Prediction of global solar irradiance based on time series analysis: Application to solar thermal power plants energy production planning. Solar Energy, 84(10), 1772–1781. [CrossRef] [Google Scholar]
- Paoli, C., Voyant, C., Muselli, M., & Nivet, M. L. (2010). Forecasting of preprocessed daily solar radiation time series using neural networks. Solar energy, 84(12), 2146–2160. [CrossRef] [Google Scholar]
- M L B., Sripriya T., Muthuraj B., Kumar D.S., Venkatesh V., Sridevi B.S., Krishna M.M.S., Rajan K. & Diriba A., (2022), “Deep Learning-Based Smart Hybrid Solar Water Heater Erection Model to Extract Maximum Energy”,International Journal of Photoenergy. [Google Scholar]
- Li, Y., Agelidis, V. G., & Shrivastava, Y. (2009, May). Wind-solar resource complementarity and its combined correlation with electricity load demand. In 2009 4th IEEE conference on Industrial electronics and applications (pp. 3623–3628). IEEE. [CrossRef] [Google Scholar]
- Su, H. I., & El Gamal, A. (2011, September). Modeling and analysis of the role of fastresponse energy storage in the smart grid. In 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton) (pp. 719–726). IEEE. [CrossRef] [Google Scholar]
- Pinson, P. (2012). Very-short-term probabilistic forecasting of wind power with generalized logit–normal distributions. Journal of the Royal Statistical Society Series C: Applied Statistics, 61(4), 555–576. [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.