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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.

References

[1]

P. Zhang, Y. Wu, and H. Zhu, Open ecosystem for future industrial Internet of things (IIoT): Architecture and application, CSEE J. Power Energy Syst., vol. 6, no. 1, pp. 1–11, 2020.

[2]

S. Pauliuk, A. Arvesen, K. Stadler, and E. G. Hertwich, Industrial ecology in integrated assessment models, Nat. Climate Change, vol. 7, no. 1, pp. 13–20, 2017.

[3]

Z. Zhou, C. Zhang, C. Xu, F. Xiong, Y. Zhang, and T. Umer, Energy-efficient industrial Internet of UAVs for power line inspection in smart grid, IEEE Trans. Ind. Informat., vol. 14, no. 6, pp. 2705–2714, 2018.

[4]

X. Cao, Z. Wen, H. Tian, D. De Clercq, and L. Qu, Transforming the cement industry into a key environmental infrastructure for urban ecosystem: A case study of an industrial city in China, J. Ind. Ecol., vol. 22, no. 4, pp. 881–893, 2018.

[5]

V. K. Chawla, A. K. Chanda, and S. Angra, Scheduling of multi load AGVs in FMS by modified memetic particle swarm optimization algorithm, J. Proj. Manag., vol. 3, no. 1, pp. 39–54, 2018.

[6]

M. Collotta, G. Pau, and V. Maniscalco, A fuzzy logic approach by using particle swarm optimization for effective energy management in IWSNs, IEEE Trans. Ind. Electron., vol. 64, no. 12, pp. 9496–9506, 2017.

[7]

E. Bryndin, Formation and management of Industry 5.0 by systems with artificial intelligence and technological singularity, Am. J. Mechan. Ind. Eng., vol. 5, no. 2, pp. 24–30, 2020.

[8]

A. Saraswat, K. Abhishek, M. R. Ghalib, A. Shankar, M. Alazab, and B. Nongpoh, Towards energy efficient approx cache-coherence protocol verified using model checker, Comput. Electr. Eng., vol. 97, p. 107482, 2022.

[9]

S. Al-Janabi and Z. Al-Janabi, Development of deep learning method for predicting DC power based on renewable solar energy and multi-parameters function, Neural Comput. Appl., vol. 35, pp. 15273–15294, 2023.

[10]

H. S. Boudet, Public perceptions of and responses to new energy technologies, Nat. Energy, vol. 4, no. 6, pp. 446–455, 2019.

[11]

G. Buccoliero, P. G. Anselma, S. A. Bonab, G. Belingardi, and A. Emadi, A new energy management strategy for multimode power-split hybrid electric vehicles, IEEE Trans. Vehicul. Technol., vol. 69, no. 1, pp. 172–181, 2020.

[12]

N. Berente, B. Gu, J. Recker, and R. Santhanam, Managing artificial intelligence, MIS Quart., vol. 45, no. 3, pp. 1433–1450, 2021.

[13]

S. Zhao, F. Blaabjerg, and H. Wang, An overview of artificial intelligence applications for power electronics, IEEE Trans. Power Electron., vol. 36, no. 4, pp. 4633–4658, 2021.

[14]

F. Schwendicke, W. Samek, and J. Krois, Artificial intelligence in dentistry: Chances and challenges, J. Dent. Res., vol. 99, no. 7, pp. 769–774, 2020.

[15]

H. Guo, J. Li, J. Liu, N. Tian, and N. Kato, A survey on space-air-ground-sea integrated network security in 6G, IEEE Commun. Surv. Tutor., vol. 24, no. 1, pp. 53–87, 2022.

[16]

R. Iqbal, M. T. Khan, H. Bilal, M. M. Aslam, I. A. Khan, S. Raja, M. Arslan, and P. M. Nguyen, Microplastics as vectors of environmental contaminants: Interactions in the natural ecosystems, Human Ecol. Risk Assessm. An Int. J., vol. 28, no. 9, pp. 1022–1042, 2022.

[17]

S. Gajendran, D. Manjula, V. Sugumaran, and R. Hema, Extraction of knowledge graph of COVID-19 through mining of unstructured biomedical corpora, Comput. Biol. Chem., vol. 102, p. 107808, 2023.

[18]

D. Wang, D. Tan, and L. Liu, Particle swarm optimization algorithm: An overview, Soft Comput., vol. 22, no. 2, pp. 387–408, 2018.

[19]

H. R. R. Zaman and F. S. Gharehchopogh, An improved particle swarm optimization with backtracking search optimization algorithm for solving continuous optimization problems, Eng. Comput., vol. 38, no. S4, pp. 2797–2831, 2022.

[20]

R. Kumar, P. Kumar, R. Tripathi, G. P. Gupta, N. Kumar, and M. M. Hassan, A privacy-preserving-based secure framework using blockchain-enabled deep-learning in cooperative intelligent transport system, IEEE Trans. Intell. Transp. Syst., vol. 23, no. 9, pp. 16492–16503, 2022.

[21]

P. Kumar, R. Kumar, G. P. Gupta, R. Tripathi, and G. Srivastava, P2TIF: A blockchain and deep learning framework for privacy-preserved threat intelligence in industrial IoT, IEEE Trans. Ind. Inform., vol. 18, no. 9, p. 6367, 2022.

[22]

P. Kumar, R. Kumar, G. P. Gupta, and R. Tripathi, BDEdge: blockchain and deep-learning for secure edge-envisioned green CAVs, IEEE Trans. Green Commun. Netw., vol. 6, no. 3, pp. 1330–1339, 2022.

[23]

R. Kumar, P. Kumar, R. Tripathi, G. P. Gupta, S. Garg, and M. M. Hassan, BDTwin: an integrated framework for enhancing security and privacy in cybertwin-driven automotive industrial Internet of Things, IEEE Internet Things J., vol. 9, no. 18, pp. 17110–17119, 2022.

[24]

G. Yeung, ‘Made in China 2025’: The development of a new energy vehicle industry in China, Area Dev. Policy, vol. 4, no. 1, pp. 39–59, 2019.

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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