Prediction of coal-fired power units carbon emission factor based on BayesianOpt-XGBoost
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.
carbon accountingcoal-fired power units carbon emission factors predictionBayesian optimizationXGBoostfeature normalization
万方数据
维普
