Impact of the tilt angle, inverter sizing factor and row spacing on the photovoltaic power forecast accuracy
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DOI: 10.1016/j.apenergy.2022.119598
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Cited by:
- Mayer, Martin János, 2022. "Benefits of physical and machine learning hybridization for photovoltaic power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
- Mayer, Martin János & Yang, Dazhi & Szintai, Balázs, 2023. "Comparing global and regional downscaled NWP models for irradiance and photovoltaic power forecasting: ECMWF versus AROME," Applied Energy, Elsevier, vol. 352(C).
- Dácil Díaz-Bello & Carlos Vargas-Salgado & Jesus Águila-León & Fabián Lara-Vargas, 2023. "Methodology to Estimate the Impact of the DC to AC Power Ratio, Azimuth, and Slope on Clipping Losses of Solar Photovoltaic Inverters: Application to a PV System Located in Valencia Spain," Sustainability, MDPI, vol. 15(3), pages 1-25, February.
- Mayer, Martin János & Yang, Dazhi, 2022. "Probabilistic photovoltaic power forecasting using a calibrated ensemble of model chains," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
- Mayer, Martin János & Yang, Dazhi, 2023. "Pairing ensemble numerical weather prediction with ensemble physical model chain for probabilistic photovoltaic power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
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Keywords
Photovoltaic modeling; Solar forecasting; Multi-objective optimization; Error propagation; Irradiance-to-power conversion;All these keywords.
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