Global solar radiation prediction using hybrid online sequential extreme learning machine model

Hou, Muzhou and Zhang, Tianle and Weng, Futian and Ali, Mumtaz and Al-Ansari, Nadhir and Yaseen, Zaher Mundher (2018) Global solar radiation prediction using hybrid online sequential extreme learning machine model. Energies, 11 (12 - Article 3415).

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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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 10 Feb 2019 23:52
Last Modified: 11 Feb 2019 04:33
Uncontrolled Keywords: global solar radiation; FOS-ELM model; input optimization; West Africa region; energy harvesting
Fields of Research : 05 Environmental Sciences > 0502 Environmental Science and Management > 050209 Natural Resource Management
Identification Number or DOI: 10.3390/en11123415
URI: http://eprints.usq.edu.au/id/eprint/35461

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