Global Solar Radiation Prediction Using Hybrid Online Sequential Extreme Learning Machine Model
Article
Article Title | Global Solar Radiation Prediction Using Hybrid Online Sequential Extreme Learning Machine Model |
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ERA Journal ID | 123161 |
Article Category | Article |
Authors | Hou, Muzhou (Author), Zhang, Tianle (Author), Weng, Futian (Author), Ali, Mumtaz (Author), Al-Ansari, Nadhir (Author) and Yaseen, Zaher Mundher (Author) |
Journal Title | Energies |
Journal Citation | 11 (12), pp. 1-19 |
Article Number | 3415 |
Number of Pages | 19 |
Year | 2018 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 1996-1073 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/en11123415 |
Web Address (URL) | https://www.mdpi.com/1996-1073/11/12/3415 |
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 (Wspeed), maximum and minimum temperature (Tmax and Tmin), maximum and minimum humidity (Hmax and Hmin), evaporation (Eo) and vapor pressure deficiency (VPD). 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 |
ANZSRC Field of Research 2020 | 410406. Natural resource management |
Byline Affiliations | Central South University, China |
School of Agricultural, Computational and Environmental Sciences | |
Lulea University of Technology, Sweden | |
Ton Duc Thang University, Vietnam | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5046/global-solar-radiation-prediction-using-hybrid-online-sequential-extreme-learning-machine-model
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Global Solar Radiation Prediction Using Hybrid OSELM.pdf | ||
License: CC BY 4.0 | ||
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