中国石油大学学报（自然科学版）2024，Vol.48Issue(4)：190-197,8.DOI:10.3969/j.issn.1673-5005.2024.04.021
基于关键特征排序的可解释碳排放预测模型
Interpretable carbon emission prediction model based on key feature ranking
摘要
Abstract
An interpretable carbon emission prediction model(EEMD-LSTM-ATT)based on the key feature ranking was proposed,where six variables were selected,i.e.the total population,the urbanization rate,the primary industry GDP,the secondary industry GDP,the tertiary industry GDP,and the total import/export trade.Using the long and short-term memory network,which has a strongly nonlinear prediction ability,as the baseline model,innovatively adopts the attention mecha-nism to extract the influencing factors and the total amount of trade.The attention mechanism was used to extract the weight information of the influencing factors and time attributes.The results show that,on one hand,the model can inhibit the gen-eration of modal overlap and reduce the influence of data nonlinearity on the model prediction;on the other hand,it can ex-plain the importance of different time attributes and different influencing factors on the carbon emission,which makes the pre-diction results interpretable.In addition,the weighting information of influencing factors and time attributes is added to the training process of the model,which can promote the organic combination of carbon emission influencing factors and model prediction.The method of this paper can achieve a high-precision carbon emission prediction,with the RMSE being 3.772,the RMAE being 3.416,and the R2 being 0.880.关键词
集合经验模态分解/长短期记忆模型/注意力机制/预测模型Key words
ensemble empirical mode decomposition/long and short-term memory model/attention mechanism/prediction model分类
资源环境引用本文复制引用
张向阳,刘树仁,刘宝亮,李长春,付占宝..基于关键特征排序的可解释碳排放预测模型[J].中国石油大学学报（自然科学版）,2024,48(4):190-197,8.基金项目
国家自然科学基金面上项目(52374067) （52374067）
