Solar irradiation nowcasting by stochastic persistence: A new parsimonious, simple and efficient forecasting tool
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DOI: 10.1016/j.rser.2018.04.116
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Cited by:
- Marchesoni-Acland, Franco & Alonso-Suárez, Rodrigo, 2020. "Intra-day solar irradiation forecast using RLS filters and satellite images," Renewable Energy, Elsevier, vol. 161(C), pages 1140-1154.
- Feng, Cong & Zhang, Jie & Zhang, Wenqi & Hodge, Bri-Mathias, 2022. "Convolutional neural networks for intra-hour solar forecasting based on sky image sequences," Applied Energy, Elsevier, vol. 310(C).
- Lopes, Francis M. & Conceição, Ricardo & Fasquelle, Thomas & Silva, Hugo G. & Salgado, Rui & Canhoto, Paulo & Collares-Pereira, Manuel, 2020. "Predicted direct solar radiation (ECMWF) for optimized operational strategies of linear focus parabolic-trough systems," Renewable Energy, Elsevier, vol. 151(C), pages 378-391.
- Francis M. Lopes & Ricardo Conceição & Hugo G. Silva & Thomas Fasquelle & Rui Salgado & Paulo Canhoto & Manuel Collares-Pereira, 2019. "Short-Term Forecasts of DNI from an Integrated Forecasting System (ECMWF) for Optimized Operational Strategies of a Central Receiver System," Energies, MDPI, vol. 12(7), pages 1-18, April.
- Rodríguez, Fermín & Martín, Fernando & Fontán, Luis & Galarza, Ainhoa, 2021. "Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power," Energy, Elsevier, vol. 229(C).
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Keywords
Prediction; Machine learning; Forecasting; Persistence; Bias-variance;All these keywords.
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