Industrial Load Forecasting Method Based on MTS-BiGRU-DMHSA
Industrial electricity consumption constitutes a significant portion of total electricity usage in China.Understanding load trends and electricity consumption data through industrial load forecasting is essential for ensuring the safe and stable operation of power grids,guiding power generation planning,and aiding industrial users in optimizing production processes and reducing costs.To address the uncertainty in industrial load fluctuations while considering electricity consumption behavior of regular industrial users,a Multi-Time Scale(MTS)feature-based industrial load forecasting method,MTS Bidirectional Gated Recurrent Unit Dual-layer Multi-Head Self-Attention(MTS-BiGRU-DMHSA),is proposed.The MTS feature fusion approach mines periodic trends and local fluctuations in industrial loads,enhancing the interpretability of load characterization.Additionally,the DMHSA mechanism utilizes attention weights to highlight important features,capturing temporal correlations through input feature correlation mining,and improving the expression of significant feature variables and key time steps.This study carries out experimental validation using data from the five-sided receiving cabinet of an industrial enterprise in China.The proposed method is compared against two evaluation indicators and five neural network-based prediction methods.The comprehensive performance advantages of the proposed method are confirmed through comparisons of computational efficiency.Results demonstrate a reduction in the error index by an average of more than 20％,with a 4.67％improvement attributed to the use of MTS features.The feasibility of applying this method to industrial load forecasting tasks is validated through visualized results and comparative analysis.
industrial load forecastingneural networkMulti-Time Scale(MTS)featureattention mechanismtime series analysis
万方数据
维普
