Forecasting energy prices using a novel hybrid model with variational mode decomposition

Forecasting energy prices accurately has always played the important role in the country's energy security and environmental impacts of policies. This paper proposes a novel decomposition-ensemble model to predict the energy prices which fluctuate wildly. The several steps process as follows: (1) The original energy prices are decomposed into sublayers with different frequencies by variational mode decomposition (VMD). (2) The autoregression model (AR) predicts the first low frequency component and Elman neural network (ELMAN) forecasts the last high frequency component. Besides, the improved bidirectional long short-term memory (IBiLSTM, the attention-based convolutional neural network and bidirectional long short-term memory) predicts other sublayers. (3) The prediction of the sublayers with different models is reconstructed as final predicted results with the non-linear integration approach. Combining econometric and artificial intelligence methods with the asymmetric feature makes the forecasting performance more accurate. The novel model outperforms other related comparative models under different training sets lengths. In general, experiments on two cases of energy prices: natural gas and carbon futures prices demonstrate the validity and reliability of the proposed model AR-IBiLSTM-ELMAN with VMD. The advanced model could simultaneously exploit the unique advantages of each model which provides an effective forecasting tool for governments and enterprises.