沈阳工业大学学报2025，Vol.47Issue(3)：302-308,7.DOI:10.7688/j.issn.1000-1646.2025.03.05
基于梯度提升决策树算法的电力工程造价预测模型
Cost prediction model of electric power engineering based on gradient boosting decision tree algorithm
摘要
Abstract
[Objective]The cost prediction of electric power engineering is of important significance in resource optimization,financial stability,risk management,efficiency improvement,project decision-making,policy formulation,market order maintenance,and investor decision-making for power grid enterprises.To address the problem of poor comprehensive performance of traditional cost prediction methods for electric power engineering,a prediction model based on a gradient boosting decision tree(GBDT)was proposed,with the small sample characteristics of power engineering cost data taken into account.The accuracy of cost prediction was improved significantly by optimizing the residuals generated during the training process.[Methods]The influencing factors of electric power engineering cost were analyzed in depth from both natural environment and technology perspectives.Eleven key variables influencing electric power engineering cost were found,and feature engineering conforming to the GBDT model was constructed through data cleaning,feature encoding,and logarithmic transformation.The hyperparameters were optimized using the Optuna framework,and the model was evaluated using a 5-fold cross validation method.The evaluation index was selected as the goodness of fit in model optimization,and the optimal hyperparameters were iteratively searched for until the prediction accuracy reached the required level or the maximum number of iterations was reached.Finally,a GBDT prediction model combined with the Optuna framework was established.The cost data of substation engineering in an area were taken as an example for experimentation,with 90％of the data samples used as training and validation sets,and the remaining 10％as test samples.The prediction results of random forest,neural network,GBDT algorithm,and the GBDT model combined with Optuna were compared,and their performances were evaluated by goodness of fit and root mean square error.[Results]The experimental results show that the prediction performance of the GBDT model combined with Optuna is better than those of random forest,neural network,and GBDT algorithm.The predicted values fluctuate within the range of±10 CNY/kVA on the basis of the true values,and the goodness of fit reaches 0.892 3,and the root mean square error is 8.01 on the validation set.The goodness of fit reaches 0.886 6,and the root mean square error is 8.09 on the test set.[Conclusion]The cost prediction model of electric power engineering based on a GBDT algorithm can predict electric power engineering cost accurately.Compared with traditional prediction methods,it has higher prediction accuracy,especially suitable for small sample datasets such as electric power engineering cost.Combined with the Optuna framework for hyperparameter optimization,it further improves the accuracy of cost prediction.Subsequent research will collect more sample data and integrate neural network algorithms to achieve better prediction results and promote the efficient operation and healthy development of power grid enterprises.关键词
电力工程/造价预测/梯度提升决策树/残差优化/对数变换/影响因子/特征工程/Optuna框架Key words
electric power engineering/cost prediction/gradient boosting decision tree/residual optimization/logarithmic transformation/influence factor/feature engineering/Optuna framework分类
信息技术与安全科学引用本文复制引用
邵帅,赵祥,敖慧凝,柳禾丰,王冬..基于梯度提升决策树算法的电力工程造价预测模型[J].沈阳工业大学学报,2025,47(3):302-308,7.基金项目
吉林省科技计划项目(J2020RCDT2B0485) （J2020RCDT2B0485）
国网吉林省电力有限公司专项(SGJLJY00JJJS2000060). （SGJLJY00JJJS2000060）
