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

Hou, Muzhou, Zhang, Tianle, Weng, Futian, Ali, Mumtaz, Al-Ansari, Nadhir and Yaseen, Zaher Mundher 2018, Global solar radiation prediction using hybrid online sequential extreme learning machine model, Energies, vol. 11, no. 12, doi: 10.3390/en11123415.

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Title Global solar radiation prediction using hybrid online sequential extreme learning machine model
Author(s) Hou, Muzhou
Zhang, Tianle
Weng, Futian
Ali, MumtazORCID iD for Ali, Mumtaz orcid.org/0000-0002-6975-5159
Al-Ansari, Nadhir
Yaseen, Zaher Mundher
Journal name Energies
Volume number 11
Issue number 12
Total pages 12
Publisher MDPI Publishing
Place of publication Basel, Switzerland
Publication date 2018-12
ISSN 1996-1073
Keyword(s) Science & Technology
Technology
Energy & Fuels
global solar radiation
FOS-ELM model
input optimization
West Africa region
energy harvesting
ARTIFICIAL NEURAL-NETWORKS
ABSOLUTE ERROR MAE
ALGORITHM
RMSE
Summary © 2018 by the authors. 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.
Language eng
DOI 10.3390/en11123415
Indigenous content off
Field of Research 09 Engineering
02 Physical Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2018, The Authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30121787

Document type: Journal Article
Collection: Faculty of Science, Engineering and Built Environment
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