综合智慧能源2025，Vol.47Issue(4)：33-40,8.DOI:10.3969/j.issn.2097-0706.2025.04.003
基于传播模型与神经网络的输变电工程造价分析与预测方法研究
Research on cost analysis and prediction methods for power transmission and transformation projects based on propagation models and neural networks
摘要
Abstract
Accurate prediction on the costs of transmission and substation projects is crucial for the planning and implementation of modern power systems.Traditional prediction methods often suffer from low accuracy and poor adaptability while handling quantitative prediction problems such as time series and structural analyses.To improve prediction accuracy,a cost prediction method for transmission and substation projects was proposed based on the Susceptible-Infected-Removed(SIR)epidemic model and neural networks.This method utilized the SIR model for dynamic modeling of variable costs,and fitted the model parameters with nonlinear least squares.Historical data and model parameters were then input into a Feedforward Neural Network(FNN),and predictions were obtained through training and computation.Finally,Bayesian optimization algorithm(BOA)was employed to optimize the hyperparameters of the FNN,completing the BOA-FNN model training.The study results indicated that this prediction method achieved a mean absolute percentage error(MAPE)as low as 0.430 7%,significantly enhancing prediction accuracy with stability and reliability.关键词
输变电工程/工程造价/传染病模型/SIR模型/前馈神经网络/贝叶斯优化算法/工程投资预测Key words
transmission and transformation engineering/project cost/epidemic model/SIR model/feedforward neural network(FNN)/Bayesian optimization algorithm(BOA)/project investment prediction分类
能源科技引用本文复制引用
陆汉东,方明,刘刚刚,周妍..基于传播模型与神经网络的输变电工程造价分析与预测方法研究[J].综合智慧能源,2025,47(4):33-40,8.基金项目
南方电网定额站研究项目(2023-10-15) China Southern Power Grid Quota Station Project(2023-10-15) （2023-10-15）
