Sampling Design Optimization of Ground Radiometric Stations

Martín-Pomares, Luis; Gastón, Martín; Polo, Jesús; Frías-Paredes, Laura; Fernández-Peruchena, Carlos M.

doi:10.1007/978-3-319-97484-2_10

Cited by 4 publications

(5 citation statements)

References 44 publications

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“…GMM supposes that there are clusters with parameters Σ, π, and µ. From Bayes rule, the probability that a pixel belongs to a Gaussian is given by Equation (6).…”

Section: N (X|µmentioning

confidence: 99%

“…For these reasons, it is necessary to have solar radiance measurement networks whose distributions must be the most representative of the climatic characteristics that influence the solar radiance that reaches the surface [4]. In this way, the identification of geographic regions with similar climatic behaviors (in this case, those related to solar radiation) will allow optimization of the deployment, management, and maintenance of the network [5,6]. Therefore, the planning and installing of a measurement network requires a previous regional analysis that allows identifying and classifying the climatic diversity of the geographical areas to be evaluated; cluster analyses have been shown to work well for regionalization [7].…”

Section: Introductionmentioning

confidence: 99%

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Multivariate Analysis for Solar Resource Assessment Using Unsupervised Learning on Images from the GOES-13 Satellite

et al. 2022

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Solar resource assessment is of paramount importance in the planning of solar energy applications. Solar resources are abundant and characterization is essential for the optimal design of a system. Solar energy is estimated, indirectly, by the processing of satellite images. Several analyses with satellite images have considered a single variable—cloudiness. Other variables, such as albedo, have been recognized as critical for estimating solar irradiance. In this work, a multivariate analysis was carried out, taking into account four variables: cloudy sky index, albedo, linke turbidity factor (TL2), and altitude in satellite image channels. To reduce the dimensionality of the database (satellite images), a principal component analysis (PCA) was done. To determine regions with a degree of homogeneity of solar irradiance, a cluster analysis with unsupervised learning was performed, and two clustering techniques were compared: k-means and Gaussian mixture models (GMMs). With respect to k-means, the GMM method obtained a smaller number of regions with a similar degree of homogeneity. The multivariate analysis was performed in Mexico, a country with an extended territory with multiple geographical conditions and great climatic complexity. The optimal number of regions was 17. These regions were compared for annual average values of daily irradiation data from ground stations using multiple linear regression. A comparison between the mean of each region and the ground station measurement showed a linear relationship with a R2 score of 0.87. The multiple linear regression showed that the regions were strongly related to solar irradiance. The optimal sites found are shown on a map of Mexico.

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“…GMM supposes that there are clusters with parameters Σ, π, and µ. From Bayes rule, the probability that a pixel belongs to a Gaussian is given by Equation (6).…”

Section: N (X|µmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multivariate Analysis for Solar Resource Assessment Using Unsupervised Learning on Images from the GOES-13 Satellite

et al. 2022

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“…Various models have been developed, improved and validated to estimate clear sky GHI data. Even though there are advanced methods to derive GHI data, ground monitoring using a pyranometer remains the most accurate way to collect data [11,12]. However, pyranometer measurements are limited or scarce due to the high costs involved in the installation, maintenance and calibration of the sensors.…”

Section: Introductionmentioning

confidence: 99%

The Performance Assessment of Six Global Horizontal Irradiance Clear Sky Models in Six Climatological Regions in South Africa

et al. 2021

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This study assesses the performance of six global horizontal irradiance (GHI) clear sky models, namely: Bird, Simple Solis, McClear, Ineichen–Perez, Haurwitz and Berger–Duffie. The assessment is performed by comparing 1-min model outputs to corresponding clear sky reference 1-min Baseline Surface Radiation Network quality controlled GHI data from 13 South African Weather Services radiometric stations. The data used in the study range from 2013 to 2019. The 13 reference stations are across the six macro climatological regions of South Africa. The aim of the study is to identify the overall best performing clear sky model for estimating minute GHI in South Africa. Clear sky days are detected using ERA5 reanalysis hourly data and the application of an additional 1-min automated detection algorithm. Metadata for the models’ inputs were sourced from station measurements, satellite platform observations, reanalysis and some were modelled. Statistical metrics relative Mean Bias Error (rMBE), relative Root Mean Square Error (rRMSE) and the coefficient of determination (R2) are used to categorize model performance. The results show that each of the models performed differently across the 13 stations and in different climatic regions. The Bird model was overall the best in all regions, with an rMBE of 1.87%, rRMSE of 4.11% and R2 of 0.998. The Bird model can therefore be used with quantitative confidence as a basis for solar energy applications when all the required model inputs are available.

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“…However, GHI monitoring stations are sparse and expensive to install and maintain. As a result, data are only available for a limited number of locations [6,[8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Given that GHI datasets are critical for better understanding of wider coverage of solar radiation [10], satellite and reanalysis based GHI datasets can be used to provide reliable alternative GHI data and compensate for the scarcity of monitoring stations by increasing the density of GHI data. The satellite or reanalysis-based datasets must first be validated by using GHI data from a good quality pyranometer [9,11,12,[16][17][18][19][20] to obtain proof of their reliability before they are used in different applications.…”

Section: Introductionmentioning

confidence: 99%

Validating Hourly Satellite Based and Reanalysis Based Global Horizontal Irradiance Datasets over South Africa

Mabasa

Lysko²,

Moloi

2021

Geomatics

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This study validates the hourly satellite based and reanalysis based global horizontal irradiance (GHI) for sites in South Africa. Hourly GHI satellite based namely: SOLCAST, Copernicus Atmosphere Monitoring Service (CAMS), and Satellite Application Facility on Climate Monitoring (CMSAF SARAH) and two reanalysis based, namely, fifth generation European Center for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5) and Modern-Era Retrospective Analysis for Research and Applications (MERRA2) were assessed by comparing in situ measured data from 13 South African Weather Service radiometric stations, located in the country’s six macro climatological regions, for the period 2013–2019. The in situ data were first quality controlled using the Baseline Surface Radiation Network methodology. Data visualization and statistical metrics relative mean bias error (rMBE), relative root mean square error (rRMSE), relative mean absolute error (rMAE), and the coefficient of determination (R2) were used to evaluate the performance of the datasets. There was very good correlation against in situ GHI for the satellite based GHI, all with R2 above 0.95. The R2 correlations for the reanalysis based GHI were less than 0.95 (0.931 for ERA5 and 0.888 for MERRA2). The satellite and reanalysis based GHI showed a positive rMBE (SOLCAST 0.81%, CAMS 2.14%, CMSAF 2.13%, ERA5 1.7%, and MERRA2 11%), suggesting consistent overestimation over the country. SOLCAST satellite based GHI showed the best rRMSE (14%) and rMAE (9%) combinations. MERRA2 reanalysis based GHI showed the weakest rRMSE (37%) and rMAE (22%) combinations. SOLCAST satellite based GHI showed the best overall performance. When considering only the freely available datasets, CAMS and CMSAF performed better with the same overall rMBE (2%), however, CAMS showed slightly better rRMSE (16%), rMAE (10%), and R2 (0.98) combinations than CMSAF rRMSE (17%), rMAE (11%), and R2 (0.97). CAMS and CMSAF are viable freely available data sources for South African locations.

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Sampling Design Optimization of Ground Radiometric Stations

Cited by 4 publications

References 44 publications

Multivariate Analysis for Solar Resource Assessment Using Unsupervised Learning on Images from the GOES-13 Satellite

Multivariate Analysis for Solar Resource Assessment Using Unsupervised Learning on Images from the GOES-13 Satellite

The Performance Assessment of Six Global Horizontal Irradiance Clear Sky Models in Six Climatological Regions in South Africa

Validating Hourly Satellite Based and Reanalysis Based Global Horizontal Irradiance Datasets over South Africa

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