%0 Journal Article
%T 基于TOPSIS-PSO-Holt指数平滑法的电网关键物资选择与价格预测<br>Selection and Price Prediction of Key Power Grid Materials Based on the TOPSIS-PSO-Holt Exponential Smoothing Method
%A 刘嫣然
%A 张勇
%A 曹楷
%A 叶湖芳
%A 宋雅茹
%A 王超
%J Hans Journal of Data Mining
%P 123-136
%@ 2163-1468
%D 2025
%I Hans Publishing
%R 10.12677/hjdm.2025.152011
%X 国网物资价格预测对预算编制和成本控制至关重要。本文基于国网江苏物资公司历史数据&#65292;以TOPSIS法筛选出了五类关键物资&#65292;并通过Holt指数平滑法构建了价格预测模型&#65292;分析了单期和多期情形下的价格趋势特征。为进一步提升预测精度&#65292;利用粒子群算法对模型超参数进行优化&#65292;通过与BP神经网络、SVR、Xgboost等模型对比&#65292;发现Holt指数平滑法表现出更高预测精度。本研究可为电网企业提供科学决策依据&#65292;提升采购合理性和经济性&#65292;增强企业市场竞争力与运营稳定性。<br>Price prediction of State Grid materials is critical for budget preparation and cost control. This study utilizes historical data from State Grid Jiangsu Material Company, employing the TOPSIS method to identify five categories of key materials. A price prediction model is constructed using the Holt exponential smoothing method, analyzing price trends and seasonal characteristics under single-period and multi-period scenarios. To improve prediction accuracy, particle swarm optimization (PSO) is applied to optimize model hyperparameters. Comparative analysis with BP neural networks and SVR demonstrates the superior accuracy of the Holt exponential smoothing method. This research provides a scientific basis for power grid enterprises to enhance procurement rationality and economic efficiency, strengthening their market competitiveness and operational stability.
%K 数据挖掘&#65292
%K 电网物资&#65292
%K 价格预测&#65292
%K TOPSIS&#65292
%K Holt指数平滑&#65292
%K 粒子群算法<br>Data Mining
%K Power Grid Materials
%K Price Prediction
%K TOPSIS
%K Holt Exponential Smoothing
%K Particle Swarm Optimization
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=110730