摘要
作为全球最大的发展中国家,中国的能源生产和碳排放位居首位。为实现碳达峰与碳中和目标,中国采取了多项应对气候变化的举措,取得明显效果。本文采用“华为杯”第二十届中国研究生数学建模竞赛D题中的数据,首先建立一个评估指标及体系,对碳排放量与经济、人口、能源消费量之间进行相关性分析,建立关联关系模型。在线性回归模型预测的基础上,引入XGboost和LSTM预测模型再次进行预测,分别得到预测结果。然后建立优化模型,以精度最小为目标函数,对三种预测方式进行加权,利用优化模型求解最优的权重,分别得到一个基于人口和经济变化的能源消费量预测模型,最终利用岭回归作为改进算法,构建碳排放量与人口、经济和能源消费量的多元线性回归的区域碳排放量预测模型。利用本文的算法,可以及时预测出区域碳排放量,使得中国能够更好、更快的走上绿色发展之路。
As the world’s largest developing country, China ranks first in terms of energy production and carbon emissions. In order to achieve the goals of carbon peak and carbon neutrality, China has taken a number of measures to combat climate change, and has achieved remarkable results. Based on the data in question D of the 20th China Graduate Mathematical Contest in Modeling, this paper first establishes an evaluation index and system, analyzes the correlation between carbon emissions and economy, population, and energy consumption, and establishes a correlation model. On the basis of linear regression model prediction, XGboost and LSTM prediction models were in-troduced to predict again, and the prediction results were obtained respectively. Then, the opti-mization model is established, the three prediction methods are weighted with the minimum pre-cision as the objective function, and the optimal weights are used to solve the optimal weights, and an energy consumption prediction model based on population and economic changes is obtained respectively, and finally the ridge regression is used as an improved algorithm to construct a re-gional carbon emission prediction model with multiple linear regression of carbon emissions and population, economy and energy consumption. Using the algorithm in this paper, regional carbon emissions can be predicted in time, so that China can better and faster embark on the path of green development.
出处
《理论数学》
2023年第12期3690-3706,共17页
Pure Mathematics
