A database infrastructure to implement real-time solar and wind power generation intra-hour forecasts
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DOI: 10.1016/j.renene.2018.02.043
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Cited by:
- Yang, Hufang & Jiang, Ping & Wang, Ying & Li, Hongmin, 2022. "A fuzzy intelligent forecasting system based on combined fuzzification strategy and improved optimization algorithm for renewable energy power generation," Applied Energy, Elsevier, vol. 325(C).
- Luis Mazorra-Aguiar & Philippe Lauret & Mathieu David & Albert Oliver & Gustavo Montero, 2021. "Comparison of Two Solar Probabilistic Forecasting Methodologies for Microgrids Energy Efficiency," Energies, MDPI, vol. 14(6), pages 1-26, March.
- Fatemi, Seyyed A. & Kuh, Anthony & Fripp, Matthias, 2018. "Parametric methods for probabilistic forecasting of solar irradiance," Renewable Energy, Elsevier, vol. 129(PA), pages 666-676.
- Yang, Dazhi & Wu, Elynn & Kleissl, Jan, 2019. "Operational solar forecasting for the real-time market," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1499-1519.
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Keywords
Renewable generation forecast; Real-time implementation; Nearest neighbors forecast;All these keywords.
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