Economy and carbon emissions optimization of different countries or areas in the world using an improved Attention mechanism based long short term memory neural network.

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.