Variational mode decomposition based random forest model for solar radiation forecasting: New emerging machine learning technology. (November 2021) - Ali, Mumtaz; Prasad, Ramendra; Xiang, Yong; Khan, Mohsin; Ahsan Farooque, Aitazaz; Zong, Tianrui; Yaseen, Zaher Mundher
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. Graphical abstract: 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.