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Construction of functional data analysis modeling strategy for global solar radiation prediction: application of cross-station paradigm
Department of Statistics, Bartin University, Bartin, Turkey.
Institute of Research and Development, Duy Tan University, Da Nang, Vietnam.
Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.ORCID iD: 0000-0002-6790-2653
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2019 (English)In: Engineering Applications of Computational Fluid Mechanics, ISSN 1994-2060, E-ISSN 1997-003X, Vol. 13, no 1, p. 1165-1181Article in journal (Refereed) Published
Abstract [en]

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 radia- tion 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 determin- ing 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 Burk- ina 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 uahigouya and Dori stations attained corre- lation 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.

Place, publisher, year, edition, pages
Taylor & Francis, 2019. Vol. 13, no 1, p. 1165-1181
Keywords [en]
Burkina Faso, functional data analysis, global solar radiation, energy harvesting, regional investigation
National Category
Geotechnical Engineering
Research subject
Soil Mechanics
Identifiers
URN: urn:nbn:se:ltu:diva-76447DOI: 10.1080/19942060.2019.1676314ISI: 000491363500001OAI: oai:DiVA.org:ltu-76447DiVA, id: diva2:1362394
Note

Validerad;2019;Nivå 2;2019-10-21 (johcin)

Available from: 2019-10-18 Created: 2019-10-18 Last updated: 2019-11-06Bibliographically approved

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