Designing a new data intelligence model for global solar radiation prediction: application of multivariate modeling scheme

Tao, Hai and Ebtehaj, Isa and Bonakdari, Hossein and Heddam, Salim and Voyant, Cyril and Al-Ansari, Nadhir and Deo, Ravinesh and Yaseen, Zaheer Mundher (2019) Designing a new data intelligence model for global solar radiation prediction: application of multivariate modeling scheme. Energies, 12 (7 - Article 1365). pp. 1-24.

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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/m2]) 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


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 20 Aug 2019 06:47
Last Modified: 21 Aug 2019 06:22
Uncontrolled Keywords: energy harvesting; solar radiation simulation; SaE-ELM model; multivariate modeling; African region
Fields of Research : 09 Engineering > 0913 Mechanical Engineering > 091305 Energy Generation, Conversion and Storage Engineering
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
05 Environmental Sciences > 0502 Environmental Science and Management > 050299 Environmental Science and Management not elsewhere classified
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
Identification Number or DOI: 10.3390/en12071365
URI: http://eprints.usq.edu.au/id/eprint/36903

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