人工神经网络
计算机科学
国内生产总值
化石燃料
人均
极限学习机
可再生能源
全球变暖
经济
环境经济学
人工智能
气候变化
经济
工程类
宏观经济学
生态学
社会学
人口学
电气工程
废物管理
生物
人口
作者
Xiaoyong Lin,Xingye Zhu,Mingfei Feng,Yongming Han,Zhiqiang Geng
标识
DOI:10.1016/j.scitotenv.2021.148444
摘要
The combustion of fossil fuels produces a large amount of carbon dioxide (CO2), which leads to global warming in the world. How to rationally consume fossil energy and control CO2 emissions has become an unavoidable problem for human beings while vigorously developing economy. This paper proposes a novel economy and CO2 emissions prediction model using an improved Attention mechanism based long short term memory (LSTM) neural network (Attention-LSTM) to analyze and optimize the energy consumption structures in different countries or areas. The Attention mechanism can add the weight of different inputs in the previous information or related factors to realize the indirect correlation between output and related inputs of the LSTM. Therefore, the Attention-LSTM can allocate more computing resources to the parts with a higher relevance of correlation in the case of limited computing power. Through inputs with the consumption of oil, natural gas, coal, hydroelectricity and renewable energy, the desirable output with the per capita gross domestic product (GDP) and the undesirable output with CO2 emissions prediction model of different countries and areas is established based on the Attention-LSTM. The experimental results show that compared with the normal LSTM, the back propagation (BP), the radial basis function (RBF) and the extreme learning machine (ELM) neural networks, the Attention-LSTM is more accurate and practical. Meanwhile, the proposed model provides guidance for optimizing energy structures to develop economy and reasonably control CO2 emissions.
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