摘要
提出基于关键特征排序的可解释碳排放预测模型(EEMD-LSTM-ATT),选取人口总数、城镇化率、第一产业国内生产总值、第二产业国内生产总值、第三产业国内生产总值与进出口贸易总额这6 个变量,以非线性预测能力强的长短时记忆网络为基线模型,采用注意力机制提取影响因素与时间属性的权重信息.结果表明:该模型一方面能够抑制模态混叠的产生,减少数据非线性对于模型预测带来的影响;另一方面能够解释不同时间属性与不同影响因素对于碳排放的重要性程度,使得预测结果具备可解释性;将影响因素与时间属性的权重信息加入模型的训练过程能够促进碳排放影响因素与模型预测有机结合;本文方法可实现高精度碳排放预测,均方根误差为 3.772,平均绝对误差为 3.416,拟合优度为 0.880.
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.
国家科技期刊平台
NSTL
万方数据