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

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Version 2 2024-06-13, 13:03
Version 1 2019-05-17, 13:43
journal contribution
posted on 2024-06-13, 13:03 authored by M Hou, T Zhang, F Weng, M Ali, N Al-Ansari, ZM Yaseen
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.

History

Journal

Energies

Volume

11

Article number

ARTN 3415

Location

Basel, Switzerland

Open access

  • Yes

eISSN

1996-1073

Language

English

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2018, The Authors

Issue

12

Publisher

MDPI