基于混合自然梯度与轻量梯度增加的电力工程成本预测方法  

Power engineering cost prediction method based on hybrid natural gradient and light gradient boosting

作　　者：宋坤 石晶 郑瑛楠 张如玉 刘伯楠 SONG Kun;SHI Jing;ZHENG Yingnan;ZHANG Ruyu;LIU Bonan(Software College,Northeastern University,Shenyang 110169,Liaoning,China;Economic and Technological Research Institute,State Grid Liaoning Electric Power Supply Co.,Ltd.,Shenyang 110015,Liaoning,China;Shenyang Power Supply Company,State Grid Liaoning Electric Power Supply Co.,Ltd.,Shenyang 110015,Liaoning,China)

机构地区：[1]东北大学软件学院,辽宁沈阳110169 [2]国网辽宁省电力有限公司经济技术研究院,辽宁沈阳110015 [3]国网辽宁省电力有限公司沈阳供电公司,辽宁沈阳110015

出　　处：《沈阳工业大学学报》2025年第2期183-189,共7页Journal of Shenyang University of Technology

基　　金：国家自然科学基金项目(61703081);国家电网科技项目(SGLNJY00ZLJS2000091)。

摘　　要：【目的】电力工程建设成本的准确预测对资源配置和决策优化至关重要。传统成本估算方法依赖于人工经验,容易受到工程项目复杂性和不确定性的影响,导致预测误差较大。近期广受关注的机器学习技术则为电力工程成本的预测提供了新的解决方案。但现有模型往往缺乏对预测结果不确定性的评估,且存在预测精度低、训练效率低、容易过拟合的缺点。本文提出了一种基于混合自然梯度与轻量梯度增加模型的电力工程成本预测方法,旨在提高预测精度,同时提供预测结果的不确定性估计。【方法】自然梯度增加模型能够估计预测值概率分布的特点,可应用于电力工程成本预测领域。然而,考虑到自然梯度增加模型在训练效率和过拟合问题中的不足,借鉴了轻量梯度增加模型的直方图优化算法,并将其融合到自然梯度增加模型中,形成了一种基于混合自然梯度与轻量梯度增加模型的电力工程成本预测方法,该模型不仅能够提高预测精度,还能够量化分析预测结果的不确定性。【结果】为验证所提模型的有效性,选用2002—2022年间发布的全真工程造价BIM数据库进行分析,该数据库包含2000条电力工程数据。提出的混合模型在测试集上表现优异,相关系数、均方根误差和平均偏置误差等指标均优于其他模型,且测试集上预测结果处于置信度为95%预测区间的概率达到了94.3%。相较于自然梯度增加模型,混合模型不仅提高了预测精度,还有效避免了过拟合问题,并在训练效率方面表现较好。【结论】本文提出的混合自然梯度与轻量梯度增加模型能够在提高预测精度的同时进行预测结果的不确定性估计,满足电力工程成本预测的多样化需求。实验验证了该模型在预测精度、泛化能力和训练效率上的优势,特别适用于复杂电力工程项目的成本估算。研究的创新之处在于提[Objective]Accurate prediction of construction costs in power engineering is crucial for resource allocation and decision optimization.Traditional cost estimation methods rely on manual experience,which are often influenced by the complexity and uncertainty of engineering projects and thus lead to a large prediction bias.In recent years,machine learning techniques have gained extensive attention,which provide new solutions for cost prediction in power engineering.However,existing models often lack uncertainty estimation for the prediction results and have problems of low prediction accuracy,low training efficiency,and being prone to overfitting.This paper proposed a power engineering cost prediction method based on a hybrid model of natural gradient boosting(NGBoost)and light gradient boosting(LGBoost),aimed at improving prediction accuracy while providing uncertainty estimation for the predicted results.[Methods]In this study,the NGBoost model,which could estimate the probability distribution of predicted values,was introduced into the field of power engineering cost prediction.However,considering the low efficiency and overfitting problems of NGBoost,the histogram optimization algorithm of LGBoost was adopted and integrated into NGBoost.This resulted in the development of a hybrid model combining NGBoost and LGBoost,which not only improved prediction accuracy but also enabled the quantification of uncertainty in the prediction results.[Results]To validate the effectiveness of the proposed model,this study used the BIM database of real engineering costs,spanning from 2002 to 2022,which included 2000 pieces of power engineering data.Experimental results show that the proposed hybrid model outperforms others in terms of correlation coefficient,root mean square error,and mean bias error.Additionally,the prediction results for the test set reach a probability of 94.3%at the 95%confidence level.Compared to NGBoost,the hybrid model not only enhances prediction accuracy but also effectively avoids overfitting and demon

关 键 词：电力工程 成本预测 自然梯度增加模型 轻量梯度增加模型 混合模型 直方图优化算法 预测结果 不确定性 

分 类 号：TM343[电气工程—电机]