College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, China; Engineering Research Center of Intelligent PSE, Ministry of Education in China, Beijing, China.
University of International Business and Economics, Beijing, China.
Sci Total Environ. 2021 Oct 20;792:148444. doi: 10.1016/j.scitotenv.2021.148444. Epub 2021 Jun 16.
The combustion of fossil fuels produces a large amount of carbon dioxide (CO), which leads to global warming in the world. How to rationally consume fossil energy and control CO emissions has become an unavoidable problem for human beings while vigorously developing economy. This paper proposes a novel economy and CO 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 CO 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 CO emissions.
化石燃料的燃烧会产生大量的二氧化碳（CO），这导致了世界范围内的全球变暖。在大力发展经济的同时，如何合理消耗化石能源和控制 CO 排放，已成为人类不可回避的问题。本文提出了一种基于改进的注意力机制的长短时记忆（LSTM）神经网络（Attention-LSTM）的新型经济和 CO 排放预测模型，用于分析和优化不同国家或地区的能源消费结构。注意力机制可以为前一信息或相关因素中的不同输入添加权重，从而实现 LSTM 的输出与相关输入之间的间接相关性。因此，在计算能力有限的情况下，Attention-LSTM 可以为相关性更高的部分分配更多的计算资源。通过输入石油、天然气、煤炭、水电和可再生能源的消耗数据，基于 Attention-LSTM 建立了不同国家和地区的人均国内生产总值（GDP）和 CO 排放预测模型的期望输出和不期望输出。实验结果表明，与正常 LSTM、反向传播（BP）、径向基函数（RBF）和极限学习机（ELM）神经网络相比，Attention-LSTM 更准确、更实用。同时，所提出的模型为优化能源结构、发展经济和合理控制 CO 排放提供了指导。
