A numerical solar radiation model based on standard meteorological observations

Atwater, Marshall A.; Ball, J. Timothy

doi:10.1016/0038-092x(78)90018-x

Cited by 129 publications

(39 citation statements)

References 11 publications

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“…Several methods to estimate incoming solar radiation using radiative transfer models and satellites have been developed around the world (Atwater and Ball, 1978;Weymouth and Le Marshall, 1994;Bastos et al, 1996;Ceballos and Moura, 1997;Dissing and Wendler, 1998;Garatuza-Payan et al, 2001;Gultepe et al, 2001). However, they have all been developed away from mountainous areas because of the lack of data for calibration and validation, and because of the complexities in topography, thus needing more complex algorithms to explain the radiative fluxes.…”

Section: Introductionmentioning

confidence: 99%

Atmospheric transmissivity: distribution and empirical estimation around the central Andes

Baigorria

Villegas

Trebejo

et al. 2004

Intl Journal of Climatology

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This study of the distribution in space and time of atmospheric transmissivity τ takes into account the fact that, in complex terrain, many factors affect this variable; thus, it is not possible to use the generalizations that can be applied under more homogeneous conditions. Climatic controls, topography and even sea currents have important effects on clouds and aerosols affecting τ , simultaneously leading to differences in the distribution of incident solar radiation. Different models exist to estimate incoming solar radiation as a function of relative sunshine hours (observed sunshine hours/theoretical sunshine hours, n/N) or differences between maximum and minimum temperatures T . We calibrated, validated and evaluated four of these empirical relations based on data from 15 weather stations in Peru. Models were calibrated using 66% of the daily historical record available for each weather station; the rest of the information was used for validation and comparison. TheÅngström-Prescott model was used to estimate incoming solar radiation based on n/N, and gave the best performance of all the models tested. The other models (Bristow-Campbell, Hargreaves, and Garcia) estimated incoming solar radiation based on T . Of all the models in this group, the Bristow-Campbell model performed best; it is also valuable because of the physical explanation involved. The empirical coefficients of all the models evaluated are presented here. Two empirical equations are proposed with which to estimate values of the coefficients b B and c B in the Bristow-Campbell model, as a function of T and latitude, allowing the model to be applied to other study areas.

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Section: Introductionmentioning

confidence: 99%

Atmospheric transmissivity: distribution and empirical estimation around the central Andes

Baigorria

Villegas

Trebejo

et al. 2004

Intl Journal of Climatology

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“…The backpropagation is the workhorse of learning with ANNs. It consists to adjust the weights of the neurons by minimizing the measure of the difference between the measured data and the predicted data obtained by (1). Inside the training, the neurons' weights are being updated by reinjection of the error inside the network.…”

Section: Feed Forward Neural Network (Ffnn)mentioning

confidence: 99%

“…Lack of long series of data or poor quality data series can combine errors in plant design, sizing, and performance forecasting, the thing that impacts negatively on the investment. Unfortunately, measures of solar radiation are usually inaccurate and rare over the world [1], especially in Morocco. Due to the measuring device's price, there is only a small number of solar stations.…”

Section: Introductionmentioning

confidence: 99%

Learning Processes to Predict the Hourly Global, Direct, and Diffuse Solar Irradiance from Daily Global Radiation with Artificial Neural Networks

Loutfi

Bernatchou

Raoui

et al. 2017

International Journal of Photoenergy

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This paper presents three different topologies of feed forward neural network (FFNN) models for generating global, direct, and diffuse hourly solar irradiance in the city of Fez (Morocco). Results from this analysis are crucial for the conception of any solar energy system. Especially, for the concentrating ones, as direct component is seldom measured. For the three models, the main input was the daily global irradiation with other radiometric and meteorological parameters. Three years of hourly data were available for this study. For each solar component's prediction, different combinations of inputs as well as different numbers of hidden neurons were considered. To evaluate these models, the regression coefficient (R 2 ) and normalized root mean square error (nRMSE) were used. The test of these models over unseen data showed a good accuracy and proved their generalization capability (nRMSE = 13.1%, 9.5%, and 8.05% and R = 0.98, 0.98, and 0.99) for hourly global, hourly direct, and daily direct radiation, respectively. Different comparison analyses confirmed that (FFNN) models surpass other methods of estimation. As such, the proposed models showed a good ability to generate different solar components from daily global radiation which is registered in most radiometric stations.

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“…(28) are defined for clear skies (see Eqs. 20, 21); α c is given by various analytical expressions (Atwater and Ball, 1978;Davies and McKay, 1982;Lyons and Edwards, 1982), as a function of n. In MRM v5, the following expression has been adopted:…”

Section: Diffuse Radiationmentioning

confidence: 99%

Performance of the meteorological radiation model during the solar eclipse of 29 March 2006

Psiloglou

Kambezidis

2007

Atmos. Chem. Phys.

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Abstract.Various solar broadband models have been developed in the last half of the 20th century. The driving demand has been the estimation of available solar energy at different locations on earth for various applications. The motivation for such developments, though, has been the ample lack of solar radiation measurements at global scale. Therefore, the main goal of such codes is to generate artificial solar radiation series or calculate the availability of solar energy at a place.One of the broadband models to be developed in the late 80's was the Meteorological Radiation Model (MRM). The main advantage of MRM over other similar models was its simplicity in acquiring and using the necessary input data, i.e. air temperature, relative humidity, barometric pressure and sunshine duration from any of the many meteorological stations.The present study describes briefly the various steps (versions) of MRM and in greater detail the latest version 5. To show the flexibility and great performance of the MRM, a harsh test of the code under the (almost total) solar eclipse conditions of 29 March 2006 over Athens was performed and comparison of its results with real measurements was made. From this hard comparison it is shown that the MRM can simulate solar radiation during a solar eclipse event as effectively as on a typical day. Because of the main interest in solar energy applications about the total radiation component, MRM focuses on that. For this component, the RMSE and MBE statistical estimators during this study were found to be 7.64% and −1.67% on 29 March as compared to the respective 5.30% and +2.04% for 28 March. This efficiency of MRM even during an eclipse makes the model promising for easy handling of typical situations with even better results.

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A numerical solar radiation model based on standard meteorological observations

Cited by 129 publications

References 11 publications

Atmospheric transmissivity: distribution and empirical estimation around the central Andes

Atmospheric transmissivity: distribution and empirical estimation around the central Andes

Learning Processes to Predict the Hourly Global, Direct, and Diffuse Solar Irradiance from Daily Global Radiation with Artificial Neural Networks

Performance of the meteorological radiation model during the solar eclipse of 29 March 2006

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