Modeling Clear Sky Solar Radiation

2017 ◽  
pp. 43-64
Keyword(s):  
2015 ◽  
Vol 14 (11) ◽  
pp. 2007-2013 ◽  
Author(s):  
Nadia Diovisalvi ◽  
Armando M. Rennella ◽  
Horacio E. Zagarese

A schematic representation of the seasonal cycle of rotifer in L. Chascomús. In this figure the relative abundances of the three dominant rotifer species are expressed as fractions of the estimated clear-sky mean daily incident solar radiation.


2000 ◽  
Vol 21 (2) ◽  
pp. 271-287 ◽  
Author(s):  
Inci Turk Toğrul ◽  
Hasan Toğrul ◽  
Duygu Evin

2019 ◽  
Vol 142 (2) ◽  
Author(s):  
Peng Li ◽  
Miloud Bessafi ◽  
Beatrice Morel ◽  
Jean-Pierre Chabriat ◽  
Mathieu Delsaut ◽  
...  

Abstract This paper focuses on the prediction of daily surface solar radiation maps for Reunion Island by a hybrid approach that combines principal component analysis (PCA), wavelet transform analysis, and artificial neural network (ANN). The daily surface solar radiation over 18 years (1999–2016) from CM SAF (SARAH-E with 0.05 deg × 0.05 deg spatial resolution) is first detrended using the clear sky index. Dimensionality reduction of the detrended dataset is secondly performed through PCA, which results in saving computational time by a factor of eight in comparison to not using PCA. A wavelet transform is thirdly applied onto each of the first 28 principal components (PCs) explaining 95% of the variance. The decomposed nine-wavelet components for each PC are fourthly used as input to an ANN model to perform the prediction of day-ahead surface solar radiation. The predicted decomposed components are finally returned to PCs and clear sky indices, irradiation in the end for re-mapping the surface solar radiation's distribution. It is found that the prediction accuracy is quite satisfying: root mean square error (RMSE) is 30.98 W/m2 and the (1 − RMSE_prediction/RMSE_persistence) is 0.409.


Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4402
Author(s):  
Julián Urrego-Ortiz ◽  
J. Alejandro Martínez ◽  
Paola A. Arias ◽  
Álvaro Jaramillo-Duque

The description and forecasting of hourly solar resource is fundamental for the operation of solar energy systems in the electric grid. In this work, we provide insights regarding the hourly variation of the global horizontal irradiance in Medellín, Colombia, a large urban area within the tropical Andes. We propose a model based on Markov chains for forecasting the hourly solar irradiance for one day ahead. The Markov model was compared against estimates produced by different configurations of the weather research forecasting model (WRF). Our assessment showed that for the period considered, the average availability of the solar resource was of 5 PSH (peak sun hours), corresponding to an average daily radiation of ~5 kWh/m2. This shows that Medellín, Colombia, has a substantial availability of the solar resource that can be a complementary source of energy during the dry season periods. In the case of the Markov model, the estimates exhibited typical root mean squared errors between ~80 W/m2 and ~170 W/m2 (~50%–~110%) under overcast conditions, and ~57 W/m2 to ~171 W/m2 (~16%–~38%) for clear sky conditions. In general, the proposed model had a performance comparable with the WRF model, while presenting a computationally inexpensive alternative to forecast hourly solar radiation one day in advance. The Markov model is presented as an alternative to estimate time series that can be used in energy markets by agents and power-system operators to deal with the uncertainty of solar power plants.


Author(s):  
D. F. Heermann ◽  
G. J. Harrington ◽  
K. M. Stahl

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