Correlogram, predictability error growth, and bounds of mean square error of solar irradiance forecasts
出版年份 2022 全文链接
标题
Correlogram, predictability error growth, and bounds of mean square error of solar irradiance forecasts
作者
关键词
-
出版物
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
Volume 167, Issue -, Pages 112736
出版商
Elsevier BV
发表日期
2022-07-01
DOI
10.1016/j.rser.2022.112736
参考文献
相关参考文献
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- (2022) Dazhi Yang et al. SOLAR ENERGY
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- (2022) Dazhi Yang et al. RENEWABLE & SUSTAINABLE ENERGY REVIEWS
- Verifying operational intra-day solar forecasts from ECMWF and NOAA
- (2022) Dazhi Yang et al. SOLAR ENERGY
- A Monte Carlo based solar radiation forecastability estimation
- (2021) Cyril Voyant et al. Journal of Renewable and Sustainable Energy
- On predictability of solar irradiance
- (2021) Xiaoyi Yang et al. Journal of Renewable and Sustainable Energy
- Solar forecasting with hourly updated numerical weather prediction
- (2021) Gang Zhang et al. RENEWABLE & SUSTAINABLE ENERGY REVIEWS
- IrradianceNet: Spatiotemporal deep learning model for satellite-derived solar irradiance short-term forecasting
- (2021) Andreas H. Nielsen et al. SOLAR ENERGY
- Choice of clear-sky model in solar forecasting
- (2020) Dazhi Yang Journal of Renewable and Sustainable Energy
- Verification of deterministic solar forecasts
- (2020) Dazhi Yang et al. SOLAR ENERGY
- Improving the McClear model estimating the downwelling solar radiation at ground level in cloud-free conditions – McClear‑v3
- (2019) Benoit Gschwind et al. METEOROLOGISCHE ZEITSCHRIFT
- A guideline to solar forecasting research practice: Reproducible, operational, probabilistic or physically-based, ensemble, and skill (ROPES)
- (2019) Dazhi Yang Journal of Renewable and Sustainable Energy
- Can we gauge forecasts using satellite-derived solar irradiance?
- (2019) Dazhi Yang et al. Journal of Renewable and Sustainable Energy
- Post-processing of NWP forecasts using ground or satellite-derived data through kernel conditional density estimation
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- Standard of reference in operational day-ahead deterministic solar forecasting
- (2019) Dazhi Yang Journal of Renewable and Sustainable Energy
- Making reference solar forecasts with climatology, persistence, and their optimal convex combination
- (2019) Dazhi Yang SOLAR ENERGY
- The National Solar Radiation Data Base (NSRDB)
- (2018) Manajit Sengupta et al. RENEWABLE & SUSTAINABLE ENERGY REVIEWS
- Short-term solar irradiance forecasting via satellite/model coupling
- (2018) Steven D. Miller et al. SOLAR ENERGY
- History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining
- (2018) Dazhi Yang et al. SOLAR ENERGY
- SolarData: An R package for easy access of publicly available solar datasets
- (2018) Dazhi Yang SOLAR ENERGY
- Very short-term irradiance forecasting at unobserved locations using spatio-temporal kriging
- (2015) Aloysius W. Aryaputera et al. SOLAR ENERGY
- Solar irradiance forecasting using spatial-temporal covariance structures and time-forward kriging
- (2013) Dazhi Yang et al. RENEWABLE ENERGY
- Comparison of numerical weather prediction solar irradiance forecasts in the US, Canada and Europe
- (2013) Richard Perez et al. SOLAR ENERGY
- Making and Evaluating Point Forecasts
- (2011) Tilmann Gneiting JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
- Validation of short and medium term operational solar radiation forecasts in the US
- (2010) Richard Perez et al. SOLAR ENERGY
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