A Bayesian-Opt-XGBoost model was established on the basis of the features of power generation units and coals,in which the parameters were optimized with Bayesian.The prediction of the carbon emission factors of power and heat generation of coal-fired power plants had coefficients of(R2)of 0.91 and 0.87,respectively,the corresponding mean absolute errors are 2.51％and 2.91％.Normalization methods were used to get rid of the dependence on coal's features,the corresponding R2 values were 0.79 and 0.77 respectively,and the mean absolute errors were 3.94％and 2.75％,the accuracy can still be acceptable.With the model,the carbon emission factors of coal power units in different provinces of China were estimated and compared with the published data,which proved the valid of this model.The analysis of the above estimated results shown that the carbon emission intensity of coal-fired power industry can be reduced by reforming the existing low-capacity units and building large capacity and high parameters units for newly plants.
关键词
碳核算:煤电碳排放因子预测/贝叶斯参数优化/XGBoost/特征标准化
Key words
carbon accounting/coal-fired power units carbon emission factors prediction/Bayesian optimization/XGBoost/feature normalization
维普
万方数据