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Designing a New Data Intelligence Model for Global Solar Radiation Prediction: Application of Multivariate Modeling Scheme

Author

Listed:
  • Hai Tao

    (Computer Science Department, Baoji University of Arts and Sciences, Baoji 721000, China)

  • Isa Ebtehaj

    (Department of Civil Engineering, Razi University, Kermanshah 97146, Iran
    Environmental Research Center, Razi University, Kermanshah 67146, Iran)

  • Hossein Bonakdari

    (Department of Civil Engineering, Razi University, Kermanshah 97146, Iran
    Environmental Research Center, Razi University, Kermanshah 67146, Iran)

  • Salim Heddam

    (Faculty of Science, Agronomy Department, Hydraulics Division, Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology, University 20 Août 1955, Route El Hadaik, BP 26, Skikda 21000, Algeria)

  • Cyril Voyant

    (Castelluccio Hospital, Radiotherapy Unit, BP 85, 20177 Ajaccio, France
    University of Reunion Island—PIMENT Laboratory, 15, Avenue René Cassin, BP 97715 Saint-Denis CEDEX, France)

  • Nadhir Al-Ansari

    (Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187 Lulea, Sweden)

  • Ravinesh Deo

    (School of Agricultural, Computational and Environmental Sciences, Centre for Sustainable Agricultural Systems & Centre for Applied Climate Sciences, Institute of Life Sciences and the Environment, University of Southern Queensland, Springfield, QLD 4300, Australia)

  • Zaher Mundher Yaseen

    (Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam)

Abstract

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 associated 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 performs 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.

Suggested Citation

  • Hai Tao & Isa Ebtehaj & Hossein Bonakdari & Salim Heddam & Cyril Voyant & Nadhir Al-Ansari & Ravinesh Deo & Zaher Mundher Yaseen, 2019. "Designing a New Data Intelligence Model for Global Solar Radiation Prediction: Application of Multivariate Modeling Scheme," Energies, MDPI, vol. 12(7), pages 1-24, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1365-:d:221246
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    References listed on IDEAS

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