Designing a new data intelligence model for global solar radiation prediction: application of multivariate modeling scheme
Article
Article Title | Designing a new data intelligence model for global solar radiation prediction: application of multivariate modeling scheme |
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ERA Journal ID | 123161 |
Article Category | Article |
Authors | Tao, Hai (Author), Ebtehaj, Isa (Author), Bonakdari, Hossein (Author), Heddam, Salim (Author), Voyant, Cyril (Author), Al-Ansari, Nadhir (Author), Deo, Ravinesh (Author) and Yaseen, Zaheer Mundher (Author) |
Journal Title | Energies |
Journal Citation | 12 (7), pp. 1-24 |
Article Number | 1365 |
Number of Pages | 24 |
Year | 2019 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 1996-1073 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/en12071365 |
Web Address (URL) | https://www.mdpi.com/1996-1073/12/7/1365 |
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 |
Keywords | energy harvesting; solar radiation simulation; SaE-ELM model; multivariate modeling; African region |
ANZSRC Field of Research 2020 | 401703. Energy generation, conversion and storage (excl. chemical and electrical) |
460207. Modelling and simulation | |
410499. Environmental management not elsewhere classified | |
Byline Affiliations | Baoji University of Arts and Sciences, China |
Razi University, Iran | |
University of 20 August 1955 of Skikda, Algeria | |
Castelluccio Hospital, France | |
Lulea University of Technology, Sweden | |
School of Agricultural, Computational and Environmental Sciences | |
Ton Duc Thang University, Vietnam | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q559w/designing-a-new-data-intelligence-model-for-global-solar-radiation-prediction-application-of-multivariate-modeling-scheme
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