摘要
[目的]在电力建设精益化投资发展约束下,提升变电站造价精准管控水平成为供电企业日益关注的问题。为了解决目前变电站工程造价影响因素繁多且复杂、工程造价难以准确预测的问题,文章提出一种基于机器学习算法的变电站工程造价预测模型。[方法]首先基于层次分析法、典型项目分析、问卷德尔菲法、相关系数法从历史变电站工程造价的数据中筛选重要造价影响因素,广泛调研并收集变电站工程造价相关数据,形成可供预测模型检验测试的大样本训练集,其次基于交叉验证与贝叶斯优化算法对支持向量回归模型进行关键参数寻优,探索造价误差较小的模型参数,最后利用寻优之后确定的支持向量回归模型进行造价预测并开展实证校验。[结果]结果显示,支持向量回归模型在保障了训练集较好拟合效果的同时,模型的泛化能力更强,在变电站工程造价总结算价格及各分部分项工程费用预测上取得了较好的准确度。[结论]通过本模型方法的运用,能形成对变电站设计阶段造价的科学预测与有效管控能力,可为实现变电站工程造价精准预测提供方法参考。
[Introduction]Under the constraint of lean investment in power construction,precise cost control of substation engineering has become an increasingly concerned issue for power suppliers.To address current difficulties in cost prediction due to the large number and great complexity of influencing factors,this paper proposes a substation engineering cost prediction model based on machine learning algorithms.[Method]Firstly,important influencing factors were selected from historical substation construction cost data using methods such as the analytic hierarchy process,analysis of typical projects,the Delphi method,and the correlation coefficient.Relevant data on substation engineering costs were collected through extensive investigation to form a substantial training dataset for model validation and testing.Then,key parameters in the Support Vector Regression(SVR)model were optimized using cross-validation and the Bayesian optimization algorithm to minimize prediction errors.Finally,the optimized SVR model was used for cost prediction,and an empirical validation was conducted.[Result]The results show that the SVR model not only demonstrates a robust fit to the training data but also excels in generalizability.It achieves good accuracy in predicting the total settlement prices of substation engineering costs as well as the costs of various sub-projects.[Conclusion]This approach enables scientific forecasting and effective management of construction costs during the substation design phase.It can offer a methodological reference for precise cost predictions in substation engineering projects.
作者
叶恺慧
YE Kaihui(China Energy Engineering Group Guangdong Electric Power Design Institute Co.,Ltd.,Guangzhou 510663,Guangdong,China)
出处
《南方能源建设》
2024年第S01期100-105,共6页
Southern Energy Construction
关键词
造价预测
变电站工程
影响因素
机器学习
支持向量机
cost prediction
substation engineering
influencing factors
machine learning
support vector machine
