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Energies 2018, 11(12), 3415; https://doi.org/10.3390/en11123415

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

1
School of Mathematics and Statistics, Central South University, Changsha 410083, Hunan, China
2
School of Agricultural, Computational and Environmental Sciences Institute of Agriculture and Environment, University of Southern Queensland, Springfield, QLD 4300, Australia
3
Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187 Lulea, Sweden
4
Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
*
Author to whom correspondence should be addressed.
Received: 12 November 2018 / Revised: 29 November 2018 / Accepted: 3 December 2018 / Published: 6 December 2018
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
Full-Text   |   PDF [3825 KB, uploaded 6 December 2018]   |  

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. View Full-Text
Keywords: global solar radiation; FOS-ELM model; input optimization; West Africa region; energy harvesting global solar radiation; FOS-ELM model; input optimization; West Africa region; energy harvesting
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Hou, M.; Zhang, T.; Weng, F.; Ali, M.; Al-Ansari, N.; Yaseen, Z.M. Global Solar Radiation Prediction Using Hybrid Online Sequential Extreme Learning Machine Model. Energies 2018, 11, 3415.

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