Variational mode decomposition based random forest model for solar radiation forecasting: New emerging machine learning technology-Reference-Cited by-同舟云学术

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Variational mode decomposition based random forest model for solar radiation forecasting: New emerging machine learning technology

Published:2021-11 Issue: Volume:7 Page:6700-6717
ISSN:2352-4847
Container-title:Energy Reports
language:en
Short-container-title:Energy Reports

Author:

Ali Mumtaz^ORCID,Prasad Ramendra,Xiang Yong,Khan Mohsin,Ahsan Farooque Aitazaz,Zong Tianrui,Yaseen Zaher Mundher

Publisher

Elsevier BV

Subject

General Energy

Reference68 articles.

1. The impact of width of the air gap channel on the mass flow rate, Rayleigh number, and efficiency of passive solar heating system;Abbas;Tikrit J. Eng. Sci.,2018

2. Proposition of new ensemble data-intelligence models for surface water quality prediction;Al-Sulttani;IEEE Access,2021

3. Improving SPI-derived drought forecasts incorporating synoptic-scale climate indices in multi-phase multivariate empirical mode decomposition model hybridized with simulated annealing and kernel ridge regression algorithms;Ali;J. Hydrol.,2019

4. Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition;Ali;Renew. Sustain. Energy Rev.,2019

5. Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts;Ali;J. Hydrol.,2020

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