Variational mode decomposition based random forest model for solar radiation forecasting: New emerging machine learning technology
Article
Article Title | Variational mode decomposition based random forest model for solar radiation forecasting: New emerging machine learning technology |
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ERA Journal ID | 210412 |
Article Category | Article |
Authors | Ali, Mumtaz, Prasad, Ramendra, Xiang, Yong, Khan, Mohsin, Farooque, Aitazaz Ahsan, Zong, Tianrui and Yaseen, Zaher Mundher |
Journal Title | Energy Reports |
Journal Citation | 7, pp. 6700-6717 |
Number of Pages | 18 |
Year | Nov 2021 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 2352-4847 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.egyr.2021.09.113 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S2352484721009203 |
Abstract | Forecasting of solar radiation (Radn) can provide an insight vision for the amount of green and friendly energy sources. Owing to the non-linearity and non-stationarity challenges caused by meteorological variables in forecasting Radn, a variational mode decomposition method is integrated with simulated annealing and random forest (VMD-SA-RF) for resolving this problem. Firstly, the input parameters are separated into training and testing phases after generating a one-day ahead significant lags at (t – 1). Secondly, the variational mode decomposition is set to factorize multivariate meteorological data of train and test sets, independently, into their band-limited signals. Thirdly, the simulate annealing based feature selection system is engaged to select the best band-limited signals. Finally, using the pertinent band-limited signals, the daily Radn is forecasted via random forest (RF) model. The outcomes are benchmarked with other comparative models. The hybrid fusion VMD-SA-RF model is tested geographically in Australia, generates reliable performance to forecast Radn. The hybrid VMD-SA-RF system combining the pertinent meteorological features, as the model predictors have substantial implications for renewable and sustainable energy resource management. |
Keywords | Solar radiation; Variational mode decomposition; Random forest; Simulated annealing; Volterra model; Energy |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460199. Applied computing not elsewhere classified |
Byline Affiliations | Deakin University |
University of Fiji, Fiji | |
Yangzhou University, China | |
University of Prince Edward Island, Canada | |
School of Mathematics, Physics and Computing |
https://research.usq.edu.au/item/w307v/variational-mode-decomposition-based-random-forest-model-for-solar-radiation-forecasting-new-emerging-machine-learning-technology
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