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• W2980646502 abstract "To support initiatives for global emissions targets set by the United Nations Framework Convention on climate change, sustainable extraction of usable power from freely-available global solar radiation as a renewable energy resource requires accurate estimation and forecasting models for solar energy. Understanding the Global Solar Radiation (GSR) pattern is highly significant for determining the solar energy in any particular environment. The current study develops a new mathematical model based on the concept of Functional Data Analysis (FDA) to predict daily-scale GSR in the Burkina Faso region of West Africa. Eight meteorological stations are adopted to examine the proposed predictive model. The modeling procedure of the regression FDA is performed using two different internal parameter tuning approaches including Generalized Cross-Validation (GCV) and Generalized Bayesian Information Criteria (GBIC). The modeling procedure is established based on a cross-station paradigm wherein the climatological variables of six stations are used to predict GSR at two targeted meteorological stations. The performance of the proposed method is compared with the panel data regression model. Based on various statistical metrics, the applied FDA model attained convincing absolute error measures and best goodness of fit compared with the observed measured GSR. In quantitative evaluation, the predictions of GSR at the Ouahigouya and Dori stations attained correlation coefficients of R = 0.84 and 0.90 using the FDA model, respectively. All in all, the FDA model introduced a reliable alternative modeling strategy for global solar radiation prediction over the Burkina Faso region with accurate line fit predictions." @default.
• W2980646502 created "2019-10-25" @default.
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• W2980646502 date "2019-01-01" @default.
• W2980646502 modified "2023-09-26" @default.
• W2980646502 title "Construction of functional data analysis modeling strategy for global solar radiation prediction: application of cross-station paradigm" @default.
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• W2980646502 doi "https://doi.org/10.1080/19942060.2019.1676314" @default.
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