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Designing a New Data Intelligence Model for Global Solar Radiation Prediction: Application of Multivariate Modeling Scheme
Computer Science Department, Baoji University of Arts and Sciences, Baoji, China.
Department of Civil Engineering, Razi University, Kermanshah, Iran. Environmental Research Center, Razi University, Kermanshah, Iran.
Department of Civil Engineering, Razi University, Kermanshah, Iran. Environmental Research Center, Razi University, Kermanshah, Iran.
Faculty of Science, Agronomy Department, Hydraulics Division, Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology, University 20 Août 1955, Algeria.
Vise andre og tillknytning
2019 (engelsk)Inngår i: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 12, nr 7, artikkel-id 1365Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Global solar radiation prediction is highly desirable for multiple energy applications, such

as energy production and sustainability, solar energy systems management, and lighting tasks for home use and recreational purposes. This research work designs a new approach and investigates the capability of novel data intelligent models based on the self-adaptive evolutionary extreme learning machine (SaE-ELM) algorithm to predict daily solar radiation in the Burkina Faso region. Four different meteorological stations are tested in the modeling process: Boromo, Dori, Gaoua and Po, located in West Africa. Various climate variables ssociated with the changes in solar radiation are utilized as the exploratory predictor variables through different input combinations used in the intelligent model (maximum and minimum air temperatures and humidity, wind speed, evaporation and vapor pressure deficits). The input combinations are then constructed based on the magnitude of the Pearson correlation coefficient computed between the predictors and the predictand, as a baseline method to determine the similarity between the predictors and the target variable. The results of the four tested meteorological stations show consistent findings, where the

incorporation of all climate variables seemed to generate data intelligent models that erforms with best prediction accuracy. A closer examination showed that the tested sites, Boromo, Dori, Gaoua and Po, attained the best performance result in the testing phase, with a root mean square error and a mean absolute error (RMSE-MAE [MJ/m 2]) equating to about (0.72-0.54), (2.57-1.99), (0.88-0.65) and (1.17-0.86), respectively. In general, the proposed data intelligent models provide an excellent modeling strategy for solar radiation prediction, particularly over the Burkina Faso region in Western Africa. This study offers implications for solar energy exploration and energy management in data sparse regions.

sted, utgiver, år, opplag, sider
MDPI, 2019. Vol. 12, nr 7, artikkel-id 1365
Emneord [en]
energy harvesting, solar radiation simulation, SaE-ELM model, multivariate modeling
HSV kategori
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Identifikatorer
URN: urn:nbn:se:ltu:diva-73542DOI: 10.3390/en12071365ISI: 000465561400182Scopus ID: 2-s2.0-85065608674OAI: oai:DiVA.org:ltu-73542DiVA, id: diva2:1303606
Merknad

Validerad;2019;Nivå 2;2019-04-15 (oliekm)

Tilgjengelig fra: 2019-04-10 Laget: 2019-04-10 Sist oppdatert: 2019-06-27bibliografisk kontrollert

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