Global horizontal irradiance clear sky models : implementation and analysis.
By Joshua S. Stein, Clifford W. Hansen, Matthew J. Reno. 2012
303 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Renewable Energy, Sustainability and the Environment
View Paper: https://www.semanticscholar.org/paper/5df99811445709d5249100120e1dc9298db5446e
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In Artificial Intelligence
- M. A. Alam
- M. Ryyan Khan
- Xingshu Sun
- Chris Deline
- Bernhard Sick
- Nils Raabe
- André Gensler
- Janosch Henze
In Renewable Energy, Sustainability and the Environment
Citations in the corpus
- Identification of periods of clear sky irradiance in time series of GHI measurements (2016, unknown): “Other methods use the variability in a time-series of irradiance measurements to classify the period as clear using metrics such as variability index (Stein et al., 2012) or probability of persistence (Kang and Tam, 2013).”
- Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks (2016, methodology): “The Clear-Sky filter uses the Clear-Sky curve [29], which estimates terrestrial solar radiation of the sun with a cloudless sky for a specific time and coordinates.”
- Optimization and performance of bifacial solar modules: A global perspective (2017, methodology): “First, we calculate the irradiance intensity of global horizontal irradiance (GHI or IGHI ) on a minute-by-minute basis by inputting the solar path into the Haurwitz clear-sky model [26]–[28] implemented in PVLIB [25].”