Spatio-temporal dynamic clustering modeling for solar irradiance resource assessment
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Maldonado Salguero, Patricia; Bueso Sánchez, María del Carmen; Molina García, Ángel; Sánchez Lozano, Juan MiguelÁrea de conocimiento
Estadística e Investigación Operativa; Matemática AplicadaPatrocinadores
These data were obtained from the NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program, United States. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. This work was partially funded by the research project PID2020–112754GB–I00, financially supported by the Ministerio de Ciencia e Innovación (Spain) .Realizado en/con
Universidad Politécnica de CartagenaFecha de publicación
2022-10-10Editorial
ELSEVIERCita bibliográfica
Patricia Maldonado-Salguero, María Carmen Bueso-Sánchez, Ángel Molina-García, Juan Miguel Sánchez-Lozano, Spatio-temporal dynamic clustering modeling for solar irradiance resource assessment, Renewable Energy, Volume 200, 2022, Pages 344-359, ISSN 0960-1481, https://doi.org/10.1016/j.renene.2022.09.113.Revisión por pares
SIPalabras clave
ClusteringFunctional data analysis
Global horizontal irradiance
Solar resource
Spatio-temporal variability
Resumen
Nowadays, with the development of international policies and agreements to promote the integration of renewable energy sources, mainly solar and wind, modeling the solar resource by including the spatio-temporal variability is crucial to determine future PV power plant locations and estimate potential power generation performances. However, contributions involving long-term periods and different time windows to explore such potential solar resource variability are generally scarce. Under this framework, the present paper proposes a methodology focused on characterizing and clustering the spatio-temporal solar resource variability through the global horizontal irradiance analysis. Hierarchical clustering technique is firstly used to classify the spatial data. Different time windows — from short-term to long-term data — can be subsequently evaluated by using various sources of information. The Spanish territory is selected as case study, considering 22-year period data (1999–2020) and ...
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