Elsevier

Energy Reports

Volume 7, November 2021, Pages 6700-6717
Energy Reports

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

https://doi.org/10.1016/j.egyr.2021.09.113Get rights and content
Under a Creative Commons license
open access

Highlights

  • An integrative hybrid solar radiation forecasting model using climate drivers was designed.

  • Variational mode decomposition splits climate drivers into band-limited sub-series (BL-IMFs).

  • Simulated annealing was applied to select pertinent features from a pool of BL-IMFs.

  • Random Forest algorithm used the selected BL-IMFs to forecast solar daily solar radiation.

  • The proposed hybrid model provides significant energy management implications.

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

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