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Open Access

Key Technology Innovation Mode of New Energy Industry Ecological Integration System Based on Particle Swarm Optimization Algorithm

School of Management, Guangzhou University, Guangzhou 510006, China
School of Finance, Guangdong Nanhua Vocational College of Industry and Commerce, Guangzhou 510507, China
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Abstract

The development of society is inseparable from the use of traditional burning energy. However, people’s excessive exploitation of fossil energy has led to the gradual shortage of fossil energy. It is essential to find New Energy (NE) and develop a new energy industry. The natural ecosystem has the characteristics of stable development. With the development of Artificial Intelligence (AI), the structure of the natural ecosystem has been applied to the NE industry, forming an NE industry ecological integration system. This paper uses Particle Swarm Optimization (PSO) algorithm to optimize the structure and resources of the NE industry, so that the NE industry has the capability of sustainable development. The traditional NE industry and the NE innovation industry ecological integration system based on PSO algorithm are compared. The experimental results show that in the NE vehicle industry, the average economic benefits of the traditional NE industry and the NE innovation industry ecosystem based on PSO algorithm are 63.6% and 77.2%, respectively. In the NE power generation industry, the average economic benefits of the traditional NE industry and the NE innovation industry ecosystem based on PSO algorithm are 67.6% and 80.4%, respectively. Therefore, in the context of AI, the application of PSO algorithm to the ecological integration system of NE industry could improve the economic benefits of NE industry.

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Tsinghua Science and Technology
Pages 1752-1762
Cite this article:
Luo S, Zhu X, Ran J. Key Technology Innovation Mode of New Energy Industry Ecological Integration System Based on Particle Swarm Optimization Algorithm. Tsinghua Science and Technology, 2024, 29(6): 1752-1762. https://doi.org/10.26599/TST.2023.9010109

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Received: 17 May 2023
Revised: 01 September 2023
Accepted: 20 September 2023
Published: 12 February 2024
© The Author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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