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 RFECVXGBoost and SHAP

作　　者：刘文新 徐文辉 陈朝晔 顾海英 温聪 姚雨龙 曾晰[2] LIU Wenxin;XU Wenhui;CHEN Zhaoye;GU Haiying;WEN Cong;YAO Yulong;ZENG Xi(Zhejiang Zheneng Wenzhou Power Generation Co.,Ltd.,Wenzhou 325000,China;Zhejiang University of Technology,Hangzhou 310014,China)

机构地区：[1]浙江浙能温州发电有限公司,浙江温州325000 [2]浙江工业大学,浙江杭州310014

出　　处：《计算机与现代化》2025年第4期63-69,共7页Computer and Modernization

基　　金：浙江浙能温州发电有限公司科技项目(ZNKT-2023-057)。

摘　　要：火电厂电力输灰系统输灰产量的准确预测,对于整个火电发电效率的提升具有非常重要的意义。目前火电厂气力输灰系统主要依赖人工经验进行操作,基于此,提出一种基于XGBoost(极端梯度提升)和SHAP(沙普利加法解释)框架的智能输灰预测模型。首先,依托火电厂电力输灰系统的DCS系统,获取空气压力、设备温度等数据信息。其次,为了提升模型预测值准确度和防止过拟合,采用RFECV(交叉验证递归特征消除法)进行特征选择,随后将选择好的特征集导入XGBoost的智能输灰预测模型中,同时采用SHAP模型进行可视化的因果分析,进而从电力输灰数据中发现有用的信息,形成火电厂电力输灰系统的知识库,以达到更加智能高效的运行目标。研究结果可对火电厂输灰预警技术,输灰系统智能化升级方面提供数据支撑,有助于火电厂电力输灰系统节能降耗。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 prediction model based on the XGBoost(eXtreme Gradient Boosting)and SHAP(Shapley Additive Explanation)framework is proposed.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 imported into the XGBoost-based intelligent ash transportation prediction model.Concurrently,the SHAP model is utilized for visual 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 SHAP 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]