Solar global horizontal and direct normal irradiation maps in Spain derived from geostationary satellites

Polo, Jesús

doi:10.1016/j.jastp.2015.05.015

Cited by 29 publications

(7 citation statements)

References 37 publications

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“…There are several satellite-derived datasets (SDDs) for establishing, measuring, modelling and estimating GHI, which can be found in [18,19]. An SDD from MSG has been used to create a solar map [20]. Studies which merged ground data with the SDD for the same above purpose reveal that the merging technique for producing a solar map is better than interpolating ground data [21,22].…”

Section: Introductionmentioning

confidence: 99%

Validation of Hourly Global Horizontal Irradiance for Two Satellite-Derived Datasets in Northeast Iraq

et al. 2018

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Several sectors need global horizontal irradiance (GHI) data for various purposes. However, the availability of a long-term time series of high quality in situ GHI measurements is limited. Therefore, several studies have tried to estimate GHI by re-analysing climate data or satellite images. Validation is essential for the later use of GHI data in the regions with a scarcity of ground-recorded data. This study contributes to previous studies that have been carried out in the past to validate HelioClim-3 version 5 (HC3v5) and the Copernicus Atmosphere Monitoring Service, using radiation service version 3 (CRSv3) data of hourly GHI from satellite-derived datasets (SDD) with nine ground stations in northeast Iraq, which have not been used previously. The validation is carried out with station data at the pixel locations and two other data points in the vicinity of each station, which is something that is rarely seen in the literature. The temporal and spatial trends of the ground data are well captured by the two SDDs. Correlation ranges from 0.94 to 0.97 in all-sky and clear-sky conditions in most cases, while for cloudy-sky conditions, it is between 0.51–0.72 and 0.82–0.89 for the clearness index. The bias is negative for most of the cases, except for three positive cases. It ranges from −7% to 4%, and −8% to 3% for the all-sky and clear-sky conditions, respectively. For cloudy-sky conditions, the bias is positive, and differs from one station to another, from 16% to 85%. The root mean square error (RMSE) ranges between 12–20% and 8–12% for all-sky and clear-sky conditions, respectively. In contrast, the RMSE range is significantly higher in cloudy-sky conditions: above 56%. The bias and RMSE for the clearness index are nearly the same as those for the GHI for all-sky conditions. The spatial variability of hourly GHI SDD differs only by 2%, depending on the station location compared to the data points around each station. The variability of two SDDs is quite similar to the ground data, based on the mean and standard deviation of hourly GHI in a month. Having station data at different timescales and the small number of stations with GHI records in the region are the main limitations of this analysis.

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Section: Introductionmentioning

confidence: 99%

Validation of Hourly Global Horizontal Irradiance for Two Satellite-Derived Datasets in Northeast Iraq

et al. 2018

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“…Their proposed model (PM) incorporated daily air temperature and saturation vapor pressure. Another method to obtain global solar irradiance data is using satellite imagery, as implemented by Polo [27]. This method processes an image captured by a satellite over a region of interest.…”

Section: Existing Statistical Global Solar Irradiance Modelsmentioning

confidence: 99%

Regression Model to Predict Global Solar Irradiance in Malaysia

Kutty

Masral

Rajendran

2015

International Journal of Photoenergy

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A novel regression model is developed to estimate the monthly global solar irradiance in Malaysia. The model is developed based on different available meteorological parameters, including temperature, cloud cover, rain precipitate, relative humidity, wind speed, pressure, and gust speed, by implementing regression analysis. This paper reports on the details of the analysis of the effect of each prediction parameter to identify the parameters that are relevant to estimating global solar irradiance. In addition, the proposed model is compared in terms of the root mean square error (RMSE), mean bias error (MBE), and the coefficient of determination (R2) with other models available from literature studies. Seven models based on single parameters (PM1 to PM7) and five multiple-parameter models (PM7 to PM12) are proposed. The new models perform well, with RMSE ranging from 0.429% to 1.774%,R2ranging from 0.942 to 0.992, and MBE ranging from −0.1571% to 0.6025%. In general, cloud cover significantly affects the estimation of global solar irradiance. However, cloud cover in Malaysia lacks sufficient influence when included into multiple-parameter models although it performs fairly well in single-parameter prediction models.

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“…Direct normal radiation can also be calculated from global and corrected diffuse horizontal radiation [11]. Geostationary satellites were used to derive solar global horizontal and direct normal irradiation maps in Spain [12].…”

Section: Introductionmentioning

confidence: 99%

Untitled

2018

IJET

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Direct Normal Radiation has been computed from global radiation for three Indian stations New Delhi, Jodhpur and Bhavnagar, because it was seen that it has a strong relation with global radiation. Here relation in the form of linear, quadratic and cubic equation, have been tried. A quadratic relation between direct normal radiation and global radiation gives best results. The differences in measured daily values of direct normal radiation and computed by the so developed quadratic relation is about ± 0.6% on daily basis. Difference on hourly basis, is within ± 11%.

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Solar global horizontal and direct normal irradiation maps in Spain derived from geostationary satellites

Cited by 29 publications

References 37 publications

Validation of Hourly Global Horizontal Irradiance for Two Satellite-Derived Datasets in Northeast Iraq

Validation of Hourly Global Horizontal Irradiance for Two Satellite-Derived Datasets in Northeast Iraq

Regression Model to Predict Global Solar Irradiance in Malaysia

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