沈阳工业大学学报2025，Vol.47Issue(3)：281-287,7.DOI:10.7688/j.issn.1000-1646.2025.03.02
基于风险分析和模糊专家系统的电力工程应急成本估算方法
Contingency cost estimation method for power engineering based on risk analysis and fuzzy expert system
摘要
Abstract
[Objective]Power engineering projects have long construction cycles and are affected by various uncertainties,which may lead to significant cost increase.Therefore,accurately estimating contingency costs is crucial for project management.However,traditional experience-based methods have large errors and are difficult to adapt to complex engineering environments.Existing methods based on fuzzy expert systems and machine learning have improved performance but still face challenges such as parameter optimization difficulties and severe overfitting.To address these issues,a new contingency cost estimation method was proposed.It leverages the advantages of adaptive network-based fuzzy inference system(ANFIS)in handling uncertainty problems and introduces a principal component analysis(PCA)module to mitigate overfitting and improve prediction accuracy.[Methods]A contingency cost estimation method integrating risk analysis and ANFIS was proposed.First,the relationship between contingency costs and 13 key risk factors affecting power engineering costs was modeled.Then,ANFIS was introduced to utilize fuzzy logic for handling uncertainty problems.ANFIS processed input variables through fuzzification and leveraged neural networks for inference,avoiding the dependency on fuzzy rule sets in traditional fuzzy expert systems.To further enhance prediction accuracy,a PCA module was incorporated into ANFIS,which eliminated redundant information through dimensionality reduction and alleviated overfitting issues associated with small datasets.[Results]In the experiments,210 power engineering contingency cost data samples were selected,with 80％randomly assigned to the training set and 20％to the test set.The performance of four methods was compared:a Mamdani fuzzy inference based method,a support vector machine(SVM)based method,an ANFIS based method,and an improved ANFIS based method.The experimental results indicate that while the ANFIS based method outperforms the existing two methods on the training set,it suffers from severe overfitting on the test set.After incorporating the PCA module,the improved ANFIS based method achieves significantly better test performance,demonstrating stronger generalization ability and faster convergence.[Conclusion]The proposed contingency cost estimation method based on the improved ANFIS combines the advantages of fuzzy inference and neural networks,enhancing prediction accuracy for power engineering contingency costs.The key innovations of this study are as follows.A contingency cost estimation method was proposed,effectively addressing the uncertainty problem by combining the advantages of fuzzy logic and neural networks.In addition,a PCA module is introduced into ANFIS,which reduces redundant information through dimensionality reduction,effectively avoids model overfitting,and improves generalization capability.The proposed method provides an intelligent solution for power engineering budget management and can be extended to other fields involving uncertainty cost estimation.关键词
应急成本/风险因素/模糊专家系统/模糊推理/自适应网络/主成分分析方法/过拟合/收敛速度Key words
contingency cost/risk factor/fuzzy expert system/fuzzy inference/adaptive network/principal component analysis method/overfitting/convergence rate分类
信息技术与安全科学引用本文复制引用
陈铭,梅诗妍..基于风险分析和模糊专家系统的电力工程应急成本估算方法[J].沈阳工业大学学报,2025,47(3):281-287,7.基金项目
广东省科技计划项目(GZHKJXM20210046). （GZHKJXM20210046）
