Enhancing the estimation of direct normal irradiance for six climate zones through machine learning models

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dc.catalogadorjlo
dc.contributor.authorRodríguez, Eduardo
dc.contributor.authorLópez Droguett, Enrique
dc.contributor.authorCardemil Iglesias, José Miguel
dc.contributor.authorStarke, Allan R.
dc.contributor.authorCornejo-Ponce, Lorena
dc.date.accessioned2024-07-18T16:36:31Z
dc.date.available2024-07-18T16:36:31Z
dc.date.issued2024
dc.description.abstractThe evaluation of solar radiation is essential for large-scale solar energy systems, as assessing economic feasibility early on depends on accurate solar radiation data. Accurate sensors are needed to characterize the solar resource. Due to a scarcity of solar radiation data, numerical models are commonly used to estimate solar radiation components using meteorological variables that are simple or cheap to measure. In recent years, the use of machine learning (ML) algorithms has gained significant popularity in the estimation of solar radiation components. In this study it is proposed a post-processing approach using the separation model outcomes as input variables to estimate the diffuse fraction. Three ML models are employed (XGBoost, Random Forest, and Multilayer Perceptron) to boost the accuracy in terms of three statistical indicators: nRMSE, nMBE, and . The employed technique takes a distinctive approach by using reference stations to train the machine learning models and, afterward, make the assessment at the site under study. The results show an improvement in terms of precision of individual separation model outcomes. Thus, the proposed methodology may serve as a reliable approach for estimating solar radiation components in cases where historical data for a specific place of interest is not accessible.
dc.format.extent38 páginas
dc.fuente.origenORCID
dc.identifier.doi10.1016/j.renene.2024.120925
dc.identifier.issn1879-0682
dc.identifier.urihttps://doi.org/10.1016/j.renene.2024.120925
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/87115
dc.information.autorucEscuela de Ingeniería; Cardemil Iglesias, José Miguel; 0000-0002-9022-8150; 119912
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final38
dc.pagina.inicio1
dc.revistaRenewable Energy
dc.rightsacceso restringido
dc.subjectDirect normal irradiance
dc.subjectSeparation model
dc.subjectMachine learning
dc.subject.ddc620
dc.titleEnhancing the estimation of direct normal irradiance for six climate zones through machine learning models
dc.typepreprint
sipa.codpersvinculados119912
sipa.trazabilidadORCID;2024-07-15
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