计算机与现代化Issue(4)：63-69,7.DOI:10.3969/j.issn.1006-2475.2025.04.010
基于RFECV-XGBoost和SHAP的火电厂电力输灰预测模型
Predictive Modeling of Ash Conveying in Thermal Power Plants Based on RFECV-XGBoost and SHAP
摘要
Abstract
Accurate prediction of ash transportation output in the power ash transportation system of thermal power plants is of great significance for improving the overall efficiency of power generation.At present,the pneumatic ash transportation systems in thermal power plants mainly rely on manual experience for operation.Based on this,an intelligent ash transportation predic-tion model based on the XGBoost(eXtreme Gradient Boosting)and SHAP(Shapley Additive Explanation)framework is pro-posed.Firstly,data such as air pressure and equipment temperature are acquired from the DCS(Distributed Control System)of the power plant's ash transportation system.Secondly,to enhance the accuracy of the model predictions and prevent overfitting,RFECV(Recursive Feature Elimination with Cross-Validation)is used for feature selection.The selected feature set is then im-ported into the XGBoost-based intelligent ash transportation prediction model.Concurrently,the SHAP model is utilized for vi-sual causal analysis,thereby discovering useful information from the power ash transportation data to form a knowledge base for the power plant's ash transportation system,aiming to achieve more intelligent and efficient operation.The research results can provide data support for early warning technology for ash transportation in thermal power plants and the intelligent upgrade of ash transportation systems,which helps to energy saving and consumption reduction in power plant ash transportation systems.关键词
火电厂/输灰预测/XGBoost模型/SHAPKey words
thermal power plant/ash conveying prediction/XGBoost model/SHAP分类
信息技术与安全科学引用本文复制引用
刘文新,徐文辉,陈朝晔,顾海英,温聪,姚雨龙,曾晰..基于RFECV-XGBoost和SHAP的火电厂电力输灰预测模型[J].计算机与现代化,2025,(4):63-69,7.基金项目
浙江浙能温州发电有限公司科技项目(ZNKT-2023-057) （ZNKT-2023-057）
