Global Solar Radiation Prediction Using Hybrid Online Sequential Extreme Learning Machine Model

Muzhou Hou, Tianle Zhang, Futian Weng, Mumtaz Ali, Nadhir Al-Ansari and Zaher Mundher Yaseen
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Muzhou Hou: School of Mathematics and Statistics, Central South University, Changsha 410083, Hunan, China
Tianle Zhang: School of Mathematics and Statistics, Central South University, Changsha 410083, Hunan, China
Futian Weng: School of Mathematics and Statistics, Central South University, Changsha 410083, Hunan, China
Mumtaz Ali: School of Agricultural, Computational and Environmental Sciences Institute of Agriculture and Environment, University of Southern Queensland, Springfield, QLD 4300, Australia
Nadhir Al-Ansari: Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187 Lulea, Sweden
Zaher Mundher Yaseen: Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

Energies, 2018, vol. 11, issue 12, 1-19

Abstract: Accurate global solar radiation prediction is highly essential for related research on renewable energy sources. The cost implication and measurement expertise of global solar radiation emphasize that intelligence prediction models need to be applied. On the basis of long-term measured daily solar radiation data, this study uses a novel regularized online sequential extreme learning machine, integrated with variable forgetting factor (FOS-ELM), to predict global solar radiation at Bur Dedougou, in the Burkina Faso region. Bayesian Information Criterion (BIC) is applied to build the seven input combinations based on speed (W speed ), maximum and minimum temperature (T max and T min ), maximum and minimum humidity (H max and H min ), evaporation (E o ) and vapor pressure deficiency (V PD ). For the difference input parameters magnitudes, seven models were developed and evaluated for the optimal input combination. Various statistical indicators were computed for the prediction accuracy examination. The experimental results of the applied FOS-ELM model demonstrated a reliable prediction accuracy against the classical extreme learning machine (ELM) model for daily global solar radiation simulation. In fact, compared to classical ELM, the FOS-ELM model reported an enhancement in the root mean square error ( RMSE ) and mean absolute error ( MAE ) by (68.8–79.8%). In summary, the results clearly confirm the effectiveness of the FOS-ELM model, owing to the fixed internal tuning parameters.

Keywords: global solar radiation; FOS-ELM model; input optimization; West Africa region; energy harvesting (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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