Attenuation Factor Estimation of Direct Normal Irradiance Combining Sky Camera Images and Mathematical Models in an Inter-Tropical Area

Nowadays, it is of great interest to know and forecast the solar energy resource that will be constantly available in order to optimize its use. The generation of electrical energy using CSP (concentrated solar power) plants is mostly affected by atmospheric changes. Therefore, forecasting solar irr...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2020-04, Vol.12 (7), p.1212
Main Authors: Mondragón, Román, Alonso-Montesinos, Joaquín, Riveros-Rosas, David, Valdés, Mauro, Estévez, Héctor, González-Cabrera, Adriana E., Stremme, Wolfgang
Format: Article
Language:English
Subjects:
Algorithms
artificial neural networks
Atmospheric models
Attenuation
Cameras
cloud detection
Clouds
Digitization
digitized image processing
Electricity
Electricity distribution
Energy industry
Energy sources
Geophysics
Irradiance
Mathematical models
Neural networks
Observatories
Operators (mathematics)
Polynomials
Power plants
Regression analysis
Regression models
Remote sensing
Satellite imagery
Short term
Sky models
Solar energy
solar irradiance estimation
solar irradiance forecasting
Solar power
Solar surface
Weather forecasting
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Summary:Nowadays, it is of great interest to know and forecast the solar energy resource that will be constantly available in order to optimize its use. The generation of electrical energy using CSP (concentrated solar power) plants is mostly affected by atmospheric changes. Therefore, forecasting solar irradiance is essential for planning a plant’s operation. Solar irradiance/atmospheric (clouds) interaction studies using satellite and sky images can help to prepare plant operators for solar surface irradiance fluctuations. In this work, we present three methodologies that allow us to estimate direct normal irradiance (DNI). The study was carried out at the Solar Irradiance Observatory (SIO) at the Geophysics Institute (UNAM) in Mexico City using corresponding images obtained with a sky camera and starting from a clear sky model. The multiple linear regression and polynomial regression models as well as the neural networks model designed in the present study, were structured to work under all sky conditions (cloudy, partly cloudy and cloudless), obtaining estimation results with 82% certainty for all sky types.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs12071212