Cost and duration prediction of power engineering projects based on linear regression and grey wolf optimization
XU Ning
LI Weijia
HONG Chong
LIU Yun
ZHOU Bo
Abstract：[Objective]Power engineering projects are typically characterized by high costs and long durations,and their construction processes are influenced by various factors such as climate conditions and material costs.Traditional methods for cost and duration prediction are mainly based on experience,which can lead to underestimated or excessive cost estimates,resulting in project delays or resource waste.With the rapid development of machine learning techniques,data-driven methods have been introduced in cost and duration prediction.However,due to the small size of datasets in power engineering,traditional machine learning models often suffer from overfitting,which limits their predictive performance.Thus,a hybrid model combining support vector regression(SVR),classification and regression trees(CART),multivariate linear regression(MLR),and grey wolf optimization(GWO)was proposed to improve prediction accuracy and generalization ability on small datasets by enhancing the update strategy and parameter search method.[Methods]The main approach of this paper was to combine machine learning models with an improved GWO(iGWO)algorithm to develop an efficient framework for predicting the cost and duration of power engineering projects.SVR,CART,and MLR models were used as baseline machine learning methods.GWO was employed to search for optimal parameters to prevent overfitting,with two improvements introduced:using chaotic sequences to initialize the wolf pack positions to ensure population diversity,and optimizing the update strategy of the grey wolves'positions and enhancing the search ability by sharing information within the surrounding pack.[Results]Experimental results show that the proposed hybrid model outperforms traditional methods in cost and duration prediction.Performance comparisons on the training and testing sets indicate that traditional machine learning models are prone to overfitting,which results in poor generalization.In contrast,the model combined with GWO improves this issue.The MLR+GWO hybrid model performs better than the other models on both the training and testing sets.Further experimental results reveal that the convergence speed of the hybrid model is significantly accelerated by the iGWO algorithm.It reaches optimal fitness within 6 to 8 iterations,while the traditional GWO algorithm requires 11 to 12 iterations to achieve similar results.Additionally,the improved algorithm effectively avoids the issue that the traditional GWO algorithm is prone to falling into local optima.[Conclusion]The hybrid model based on linear regression and iGWO demonstrates performance advantages in predicting costs and durations of power engineering projects.The iGWO algorithm enhances the global search capability and convergence speed through optimized initialization sequences and update strategies.The proposed hybrid model exhibits better generalization performance,compared to traditional machine learning models.Compared with traditional methods,this approach performs better in terms of prediction accuracy and training efficiency.
Keywords：power engineeringcost predictionduration predictionsupport vector regressiondecision treelinear regressiongrey wolf optimization algorithmchaotic sequence
Publication Date：2025-05-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 295-301 )