山东电力技术2024，Vol.51Issue(1)：77-84,8.DOI:10.20097/j.cnki.issn1007-9904.2024.01.009
基于EABC算法优化RFR模型的电力行业碳排放量预测
Forecast of Electricity Industry Carbon Emission Based on EABC Algorithm Optimized RFR Model
摘要
Abstract
In order to solve the forecast problem of electricity industry carbon emissions,the forecast model based on evolve artificial bee colony(EABC)algorithm optimized random forest regression(RFR)was proposed.Firstly,the influence factors of electricity industry carbon emission were determined based on the STIRPAT model,which were considered as the input independent variables of forecast model.Then,the RFR model was optimized by EABC algorithm,and the adverse influence on prediction accuracy due to unreasonable subjective setting of model parameters can be avoid.Finally,the parameter optimized model was used to forecast the electricity industry carbon emission.The verification result of actual measured data shows that the proposed model can accurately reflect the future carbon emission trend of electricity industry,and the model has higher forecast accuracy and more obvious advantage compared with the other forecast models,which can provide a certain reference for policy formulation of energy conservation and emission reduction.关键词
碳排放/STIRPAT模型/进化人工蜂群算法/随机森林回归模型Key words
carbon emission/STIRPAT model/EABC algorithm/RFR model分类
环境科学引用本文复制引用
赵中华,张绪辉,王太,刘科,张利孟..基于EABC算法优化RFR模型的电力行业碳排放量预测[J].山东电力技术,2024,51(1):77-84,8.基金项目
国网山东省电力公司电力科学研究院自主研发项目"碳电协同背景下电力系统碳评价与低碳调度技术研究"(520626220067).Independent Research and Development Project of State Grid Shandong Electric Power Research Institute"Research on electric system carbon assessment and low carbon dispatch technology under carbon electric synergy background"(520626220067). （520626220067）
