参数优化变分模态分解与LSTM的电力物资需求预测

国家电网物资采购管理水平不断提高，线上采购流程逐步完善，但仍存在由于采购计划预估不准导致招投标过程中，供应商利用招投标总标包机制进行价格博弈而造成电网公司采购成本增加，因此，建立准确有效的电力物资需求预测模型具有重要意义。针对电力物资序列的非稳定性、波动性和间歇性特点，提出一种基于参数优化变分模态分解(variational mode decomposition, VMD)与长短时记忆神经网络(long short-term memory, LSTM)的电力物资需求预测方法，选取国网电商专区平台的典型电力物资，采用鲸鱼优化算法(whale optimization algorithm, WOA)参数优化的VMD对原始序列进行模态分解，将分解获得的各模态分量分别构建LSTM模型，最后将各模态的预测值叠加重构为电力物资的预测值。实验结果表明：所提电力物资需求预测方法较LSTM、EMD-LSTM、VMD-LSTM、PSO-VMD-LSTM、SSA-VMD-LSTM有更高的准确率，对电网物资采购预测具有一定实际意义。

A power material demand forecasting method based on parameter optimization variational mode decomposition and LSTM

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.