Price prediction of power transformer materials based on CEEMD and GRU

The rapid growth of the Chinese economy has fueled the expansion of power grids. Power transformers are key equipment in power grid projects, and their price changes have a significant impact on cost control. However, the prices of power transformer materials manifest as nonsmooth and nonlinear sequences. Hence, estimating the acquisition costs of power grid projects is difficult, hindering the normal operation of power engineering construction. To more accurately predict the price of power transformer materials, this study proposes a method based on complementary ensemble empirical mode decomposition (CEEMD) and gated recurrent unit (GRU) network. First, the CEEMD decomposed the price series into multiple intrinsic mode functions (IMFs). Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each IMF. Then, an empirical wavelet transform (EWT) was applied to the aggregation sequence with a large sample entropy, and the multiple subsequences obtained from the decomposition were predicted by the GRU model. The GRU model was used to directly predict the aggregation sequences with a small sample entropy. In this study, we used authentic historical pricing data for power transformer materials to validate the proposed approach. The empirical findings demonstrated the efficacy of our method across both datasets, with mean absolute percentage errors (MAPEs) of less than 1% and 3%. This approach holds a significant reference value for future research in the field of power transformer material price prediction.