重庆大学学报2024，Vol.47Issue(4)：127-138,12.DOI:10.11835/j.issn.1000.582X.2024.04.011
参数优化变分模态分解与LSTM的电力物资需求预测
A power material demand forecasting method based on parameter optimization variational mode decomposition and LSTM
摘要
Abstract
The State Grid has continuously improved its material procurement management level and refined its online procurement processes.However,inaccurate estimation of procurement plans,has led suppliers to engage in price games using the general bidding and tendering mechanism during the bidding process.This has resulted in increased procurement costs of the power grid company.Therefore,it is of great significance to establish an accurate and effective electricity material demand forecasting model.In respose to the instability,volatility and intermittency of power material sequences,this paper proposes a forecasting method for power material demand based on parameter-optimized variational mode decomposition(VMD)and long short-term memory neural network(LSTM).Typical power materials from the State Grid e-commerce zone platform were selected.VMD,optimized by using the whale optimization algorithm(WOA)parameters,was adopted to perform modal decomposition on the original sequence.LSTM models were then constructed for each modal component obtained from the decomposition.Finally,the predicted values of each mode were superimposed and reconstructed into the predicted value of power materials.Experimental results show that the proposed method achieves higher prediction accuracy compared to LSTM,EMD-LSTM,VMD-LSTM,PSO-VMD-LSTM and SSA-VMD-LSTM.This approach holds practical significance for the forecast of power grid material purchase.关键词
电力物资/长短期记忆神经网络/变分模态分解/鲸鱼优化算法/时间序列Key words
power materials/long short-term memory neural network/variational mode decomposition/whale optimization algorithm/time series分类
信息技术与安全科学引用本文复制引用
向洪伟,曹馨雨,张丽娟,周楚婷,张迪,邓晨凤,谢鸿鹏,王楷..参数优化变分模态分解与LSTM的电力物资需求预测[J].重庆大学学报,2024,47(4):127-138,12.基金项目
国家电网有限公司科技资助项目(SGCQWZ00ZBJS2313256).Supported by State Grid Corporation of China Science and Technology Project(SGCQWZ00ZBJS2313256). （SGCQWZ00ZBJS2313256）
