OPTIMIZATION OF LINEAR MODELS FOR ESTIMATION OF GLOBAL SOLAR IRRADIANCE THROUGH REMOTE SENSING PRODUCTS IN BOTUCATU (SP) - BRAZIL
DOI:
https://doi.org/10.59627/cbens.2024.2411Keywords:
Modelagem da Radiação Solar, Previsão da Energia Solar, Regressão Linear MultivariadaAbstract
There is a consensus that knowledge about solar irradiance levels obtained on the Earth's surface through automatic sensors represents the most desirable situation. However, the construction of solarimetric stations involves a high financial contribution, which makes it difficult to implement this scenario in many locations in Brazil. The aim of this study was to create three linear regression models, with different input parameter combinations, to estimate hourly global solar irradiance on the Earth's surface, using information obtained by the Global Land Data Assimilation System 2.1. An assessment was carried out to determine which combination of independent linear regression variables would best fit the measurements collected at the School of Agricultural Sciences (UNESP) in Botucatu (SP) - Brazil, during the period 2020-2022. Therefore, three linear regression models were created in Python using global solar irradiance, provided by the remote sensing product, atmospheric transmissivity index and solar elevation, as independent variables, on an hourly time scale. The evaluation of the linear regression models was based on the statistical indicators MBE, rMBE, RMSE, rRMSE and R², using as a reference measurement obtained by a pyranometer on the Earth's surface. The first linear fit combination produced an R² of 0.83, with an rMBE of 18.13% and an rRMSE of 26.63%. Likewise, the second linear adjustment combination generated an R² of 0.88, with an rMBE of 15.69% and an rRMSE of 26.31%. Finally, the third linear adjustment combination presented an R² of 0.90, with an rMBE of 15.52% and an rRMSE of 20.75%. Using the solar irradiance provided by the remote sensing product, atmospheric transmissivity index and solar elevation, as independent variables, created the linear regression model that better understood the atmospheric and astronomical processes that occurred, allowing to obtain estimates of solar irradiance on te Earth's surface with greater precision.
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References
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