Deep Learning-Based Economic Forecasting for the New Energy Vehicle Industry

Bowen Cai,Naeem Jan

doi:10.1155/2021/3870657

Abstract

Recently the issues of insufficient energy and serious air pollution around the world have been rising. Henceforth, there is a need to carry out a research of new energy. Soon, new energy vehicles will be the mainstream trend, which can not only reduce the burden of consumers due to rising fuel prices but also solve the air pollution problem caused by the exhaust emissions of fuel vehicles. With the rapid development of science and technology, deep learning continues to make breakthroughs, and, in the field of economy with huge information data, we have more powerful weapons available to predict and research important economic data with infinite value, which can not only provide reference information to policy makers but also help enterprises and even economic markets to develop more healthily and sustainably. Therefore, this article uses deep learning algorithms to forecast and analyze the new energy industry, starting from the financial information released by new energy vehicle companies in their annual reports, in order to make basic judgments and help policy makers and enterprises in the new energy vehicle industry.

Highlights

Since the industrial revolution, a series of ecological and environmental problems [1] brought about by excessive human production and consumption [2] have come to a point where we have to pay attention to them and take measures to remedy and correct them
Root Mean Square Error (RMSE) is the value obtained after the root of the variance between the predicted value and the true value, and the RMSE is a clearer measure of the prediction result than the MSE and better represents the deviation between the true value and the predicted value, because the root of the error unit can be kept constant. e calculation formula is shown in (13), where y􏽢t represents the predicted value and yi represents the true value
In the recurrent neural network (RNN) model, it is created by a layer of SimpleRNN (128). e LSTM model and the GRU model are created by LSTM/ GRU (128, input_shape, return_sequences False), respectively. ese comparison models were trained and tested with the convolutional neural network (CNN)-GRU model and CNN + GRU fusion model constructed in this paper, using the same training and test sets. e experimental results are shown in Tables 1 and 2

Summary

Introduction

A series of ecological and environmental problems [1] brought about by excessive human production and consumption [2] have come to a point where we have to pay attention to them and take measures to remedy and correct them. In this context, the concept of low-carbon development [3], which balances environmental protection and development, has gradually emerged and become a global trend. Economic and social development and environmental protection have always been a dilemma for human beings [4], especially after the industrialization era. From the current energy production and consumption situation, the development of new energy and energy-efficient technologies and products is an important step to ensure the sustainable development of the global economy

Objectives

Results

Conclusion