New Temperature-based Models for Predicting Global Solar Radiation

Hassan, Gasser E.; Youssef, M. Elsayed; Mohamed, Zahraa E.; Ali, Mohamed A.; Hanafy, Ahmed A.

doi:10.1016/j.apenergy.2016.07.006

Cited by 164 publications

(71 citation statements)

References 47 publications

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“…In general, the sunshinebased models are more accurate than temperature-based models [11,25]. However, sunshine data are not widely available compared with ambient temperature data at standard meteorological stations [11].…”

Section: Advances In Meteorologymentioning

confidence: 99%

“…Liu et al [10] evaluated the accuracy and applicability of 16 temperaturebased models, including modified versions of the BC and HS models in 15 meteorological stations of Northeast China, North China Plain, and Northwest China, and the results showed that the original BC model performed similarly to the best performing modified HS model but significantly outperformed the original HS model with a 4∼7% higher accuracy. Hassan et al [11] established 17 new temperaturebased models and compared these models with Annandale, Allen, and Goodin models to estimate monthly average daily in Egypt and found that the local formula for the most accurate new model provided good predictions at different locations, especially at coastal sites. In general, the sunshinebased models are more accurate than temperature-based models [11,25].…”

Section: Advances In Meteorologymentioning

confidence: 99%

“…Hassan et al [11] established 17 new temperaturebased models and compared these models with Annandale, Allen, and Goodin models to estimate monthly average daily in Egypt and found that the local formula for the most accurate new model provided good predictions at different locations, especially at coastal sites. In general, the sunshinebased models are more accurate than temperature-based models [11,25]. However, sunshine data are not widely available compared with ambient temperature data at standard meteorological stations [11].…”

Section: Advances In Meteorologymentioning

confidence: 99%

“…Thus, different models have been proposed for estimating daily or monthly using different techniques, such as geostationary satellite images, neural networks, time series methods, physically radiative transfer models, and stochastic weather methods, which were generally based on different types of data including meteorological and geographical data [11,12]. Among them, meteorological data-based models using empirical correlations depend on the most common meteorological elements including cloud cover, sunshine duration, temperature, and relative humidity, making them the most commonly examined and widely used models around the world, especially the sunshine-based and temperature-based models [6,11].…”

Section: Advances In Meteorologymentioning

confidence: 99%

“…Among them, meteorological data-based models using empirical correlations depend on the most common meteorological elements including cloud cover, sunshine duration, temperature, and relative humidity, making them the most commonly examined and widely used models around the world, especially the sunshine-based and temperature-based models [6,11]. The primary sunshinebased model can be traced back toÅngström model, using sunshine duration and clear sky radiation data to estimate [11,13,14]. Prescott [15] suggested using the extraterrestrial radiation to replace clear sky radiation and presented theÅngström-Prescott (AP) model.…”

Section: Advances In Meteorologymentioning

confidence: 99%

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Comparative Analysis of Global Solar Radiation Models in Different Regions of China

Zhang

Cui

Feng

et al. 2018

Advances in Meteorology

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Complete and accurate global solar radiation ( ) data at a specific region are crucial for regional climate assessment and crop growth modeling. The objective of this paper was to evaluate the capability of 12 solar radiation models based on meteorological data obtained from 21 meteorological stations in China. The results showed that the estimated and measured daily had statistically significant correlations ( < 0.01) for all the 12 models in 7 subzones of China. The Bahel model showed the best performance for daily estimation among the sunshine-based models, with average 2 of 0. , and average NS of 0.696, respectively. On monthly scale,Ögelman model showed the best performance among the sunshine-based models, with average RE of 5.66%. The BC model showed the best performance among the temperature-based models, with average RE of 8.26%. Generally, the sunshine-based models were more accurate than the temperature-based models. Overall, the Bahel model is recommended to estimate daily ,Ögelman model is recommended to estimate monthly average daily in China when the sunshine duration is available, and the BC model is recommended to estimate both daily and monthly average daily when only temperature data are available.

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Section: Advances In Meteorologymentioning

confidence: 99%

Section: Advances In Meteorologymentioning

confidence: 99%

Section: Advances In Meteorologymentioning

confidence: 99%

Section: Advances In Meteorologymentioning

confidence: 99%

Section: Advances In Meteorologymentioning

confidence: 99%

See 3 more Smart Citations

Comparative Analysis of Global Solar Radiation Models in Different Regions of China

Zhang

Cui

Feng

et al. 2018

Advances in Meteorology

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On the calibration and applicability of global solar radiation models based on temperature extremities in India

Jamil

Irshad

Algahtani

et al. 2019

Env Prog and Sustain Energy

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In the present work, global solar radiation models based on the difference of monthly‐average daily ambient temperature extremes are calibrated for country‐wide application in India. Solar radiation measurements obtained from the Indian Meteorological Department for 22 locations of India for a duration of 15 years (1986–2000) were used. Solar radiation models based on temperature extremes were reviewed and 33 forms were considered for analysis. The models were derived using all the collected data by fitting a model form and extracting the coefficients with the help of DataFit® software. The calibrated models were analyzed and compared using statistical tools and ranked using Global Performance Indicator (GPI) where a higher value represented a model's better performance. Then, the models were arranged using GPI and it was found that model form 33 (fourth order polynomial) resulted in best estimation capability with a GPI of 0.0763. Further, Model 23, Model 28, Model 24, and Model 26 were also among the top models with GPI values of 0.0655, 0.0587, 0.0478, and 0.0426, respectively. Best ranking models were also used to obtain estimates for representative sites in regions with different climate characteristics within India and it was found that the models performed reasonably well.

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Development of machine learning models based on air temperature for estimation of global solar radiation in India

Husain

Khan

2021

Env Prog and Sustain Energy

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The designing of solar thermal systems need accurate information on global solar radiation (GSR). In the present study, six machine learning models, for example, random forest, k‐nearest neighbors, Gaussian process regression, support vector machine, multilayer perception, and XGBoost, are developed for GSR prediction with only air temperature as input for different climatic zones of India. The performance of machine learning models is also compared with some well‐known empirical models. The results show that generally, the performance of the machine learning models is better than empirical models, though, for a few climatic zones, empirical models give a better prediction. The top‐performing models are k‐nearest neighbors and XGBoost. Thus, we highly recommend temperature‐based models to predict GSR in the regions of India where only air temperature data are available. The accurate information of future GSR can be easily obtained by combining future air temperature forecasts with KNN/XGBoost models. These models can be extremely helpful in designing solar thermal systems in those regions where solar radiation facility is not available.

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New Temperature-based Models for Predicting Global Solar Radiation

Cited by 164 publications

References 47 publications

Comparative Analysis of Global Solar Radiation Models in Different Regions of China

Comparative Analysis of Global Solar Radiation Models in Different Regions of China

On the calibration and applicability of global solar radiation models based on temperature extremities in India

Development of machine learning models based on air temperature for estimation of global solar radiation in India

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