全球能源互联网（英文）2024，Vol.7Issue(2)：217-227,11.DOI:10.1016/j.gloei.2024.04.009
基于CEEMD和GRU的电力变压器材料价格预测
Price prediction of power transformer materials based on CEEMD and GRU
摘要
Abstract
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.关键词
电力变压器材料/价格预测/互补集合经验模态分解/门控循环单元/经验小波变换Key words
Power transformer material/Price prediction/Complementary ensemble empirical mode decomposition/Gated recurrent unit/Empirical wavelet transform引用本文复制引用
黄琰,胡玉峰,吴良峥,文上勇,万正东..基于CEEMD和GRU的电力变压器材料价格预测[J].全球能源互联网（英文）,2024,7(2):217-227,11.基金项目
This work was supported by China Southern Power Grid Science and Technology Innovation Research Project(000000KK52220052). （000000KK52220052）
