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| Titel |
Temperature based daily incoming solar radiation modeling based on gene expression programming, neuro-fuzzy and neural network computing techniques. |
| VerfasserIn |
G. Landeras, J. J. López, O. Kisi, J. Shiri |
| Konferenz |
EGU General Assembly 2012
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 14 (2012) |
| Datensatznummer |
250060716
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| Zusammenfassung |
| The correct observation/estimation of surface incoming solar radiation (RS) is very important
for many agricultural, meteorological and hydrological related applications. While most
weather stations are provided with sensors for air temperature detection, the presence of
sensors necessary for the detection of solar radiation is not so habitual and the data quality
provided by them is sometimes poor. In these cases it is necessary to estimate this
variable.
Temperature based modeling procedures are reported in this study for estimating daily
incoming solar radiation by using Gene Expression Programming (GEP) for the first time,
and other artificial intelligence models such as Artificial Neural Networks (ANNs),
and Adaptive Neuro-Fuzzy Inference System (ANFIS). Traditional temperature
based solar radiation equations were also included in this study and compared with
artificial intelligence based approaches. Root mean square error (RMSE), mean
absolute error (MAE) RMSE-based skill score (SSRMSE), MAE-based skill score
(SSMAE) and r2 criterion of Nash and Sutcliffe criteria were used to assess the models’
performances.
An ANN (a four-input multilayer perceptron with ten neurons in the hidden layer)
presented the best performance among the studied models (2.93 MJ m-2 d-1 of RMSE). A
four-input ANFIS model revealed as an interesting alternative to ANNs (3.14 MJ m-2 d-1 of
RMSE). Very limited number of studies has been done on estimation of solar radiation based
on ANFIS, and the present one demonstrated the ability of ANFIS to model solar radiation
based on temperatures and extraterrestrial radiation. By the way this study demonstrated, for
the first time, the ability of GEP models to model solar radiation based on daily atmospheric
variables. Despite the accuracy of GEP models was slightly lower than the ANFIS and
ANN models the genetic programming models (i.e., GEP) are superior to other
artificial intelligence models in giving a simple explicit equation for the phenomenon
which shows the relationship between the input and output parameters. This study
provided new alternatives for solar radiation estimation based on temperatures. |
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