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

Hai Tao, Isa Ebtehaj, Hossein Bonakdari, Salim Heddam, Cyril Voyant, Nadhir Al-Ansari, Ravinesh Deo and Zaher Mundher Yaseen
Additional contact information
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
Hossein Bonakdari: Department of Civil Engineering, Razi University, Kermanshah 97146, 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
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

Energies, 2019, vol. 12, issue 7, 1-24

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.

Keywords: energy harvesting; solar radiation simulation; SaE-ELM model; multivariate modeling; African region (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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