Analysis of a solarimetric database for Mexico and comparison with the CSR model

Riveros-Rosas, David; Arancibia-Bulnes, Camilo A.; Bonifaz, R.; Medina, Méndez; Peón, R.; Valdés, M.

doi:10.1016/j.renene.2014.09.013

Cited by 15 publications

(10 citation statements)

References 9 publications

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“…The National Weather Service (SMN due to the Spanish acronym) monitors a network of 180 automatic weather stations (called EMAS due to the Spanish acronym), each with a pyranometer Kipp & Zonen (R) model CMP11 to measure global solar irradiance. However, the stations were installed with hydrometeorological criteria, and most stations have the pyranometers installed in inappropriate conditions [28]. An analysis was carried out to identify measuring issues in the database, and 26 stations were selected to calculate the daily irradiation for the year 2015.…”

Section: Relationship Between Clusters and The Solar Radiationmentioning

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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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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Section: Relationship Between Clusters and The Solar Radiationmentioning

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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“…The meteorological stations from the SMN selected were determined utilizing a selection criterion to avoid data gaps in the record and ensure proper data to analyze and compare against the model results. This criterion is based on the restrictions discussed in [8]. The selection conditions are as follows:…”

Section: Smnmentioning

confidence: 99%

“…Root mean square error (RMSE) represents the standard deviation of the calculated errors. The smaller the value, the better the accuracy [8,13]. It can be obtained by…”

Section: Statistical Metricsmentioning

confidence: 99%

Estimation of Solar Resource Based on Meteorological and Geographical Data: Sonora State in Northwestern Territory of Mexico as Case Study

et al. 2020

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A model developed at the University of Tomsk, Russia, for high latitudes (over 55° N) is proposed and applied to the analysis and observation of the solar resource in the state of Sonora in the northwest of Mexico. This model utilizes satellite data and geographical coordinates as inputs. The objective of this research work is to provide a low-cost and reliable alternative to field meteorological stations and also to obtain a wide illustration of the distribution of solar power in the state to visualize opportunities for sustainable energy production and reduce its carbon footprint. The model is compared against real-time data from meteorological stations and satellite data, using statistical methods to scrutinize its accuracy at local latitudes (26–32° N), where a satisfactory performance was observed. An annual geographical view of available solar radiation against maximum and minimum temperatures for all the state municipalities is provided to identify the photovoltaic electricity generation potential. The outcomes are proof that the model is economically viable and could be employed by local governments to plan solar harvesting strategies. The results are generated from an open source model that allows calculating the available solar radiation over specific land areas, and the application potential for future planning of solar energy projects is evident.

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“…In addition, having stations implies operation and maintenance costs, which is also a constraint. Moreover, it has been reported that a significant number of stations may have erroneous measurements or that they have not met certain validation criteria [18]. Another study reported that only 33% of the stations in the state of Sonora (northwest of Mexico) were reliable, which is due to, among other causes, the possible deficient maintenance of the stations.…”

Section: Introductionmentioning

confidence: 99%

Solar Irradiation Evaluation through GIS Analysis Based on Grid Resolution and a Mathematical Model: A Case Study in Northeast Mexico

et al. 2021

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The estimation of the solar resource on certain surfaces of the planet is a key factor in deciding where to establish solar energy collection systems. This research uses a mathematical model based on easy-access geographic and meteorological information to calculate total solar radiation at ground surface. This information is used to create a GIS analysis of the State of Nuevo León in Mexico and identify solar energy opportunities in the territory. The analyzed area was divided into a grid and the coordinates of each corner are used to feed the mathematical model. The obtained results were validated with statistical analyses and satellite-based estimations from the National Aeronautics and Space Administration (NASA). The applied approach and the results may be replicated to estimate solar radiation in other regions of the planet without requiring readings from on-site meteorological stations and therefore reducing the cost of decision-making regarding where to place the solar energy collection equipment.

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Analysis of a solarimetric database for Mexico and comparison with the CSR model

Cited by 15 publications

References 9 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

Estimation of Solar Resource Based on Meteorological and Geographical Data: Sonora State in Northwestern Territory of Mexico as Case Study

Solar Irradiation Evaluation through GIS Analysis Based on Grid Resolution and a Mathematical Model: A Case Study in Northeast Mexico

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