Application of Computer Big Data Technology in Urban Power Saving

Authors

  • Qiuxin Si Zibo Vocational Institute, Zibo, Shandong 255300, China

Keywords:

Computer, Big Data Technology, Urban Power Conservation

Abstract

Electric power system as an important link of the city and social functioning, has a great impact on people's daily work life level, and as a result of the limitation of traditional technology, the power system operation process will produce great energy loss, but also increases the speed of the natural resource consumption, hindering the healthy development of the society as a whole; with the concept of energy conservation and environmental protection is widely used in all walks of life, in the modern city power system design, operation management can strengthen the application of the technology such as computer big data, operation in power system with scientific control, to meet people's daily life and city of stable operation at the same time, energy saving electric energy and the consumption of natural resources, lay a solid foundation for the healthy development of electric power industry and society as a whole. The following is mainly to analyze and explore the application of computer big data technology in urban power conservation.

References

Lyu, T., Gu, D., Chen, P., Jiang, Y., Zhang, Z., Pang, H., ... & Dong, Y. (2024). Optimized CNNs for Rapid 3D Point Cloud Object Recognition. arXiv preprint arXiv:2412.02855.

Yin, Y., Xu, G., Xie, Y., Luo, Y., Wei, Z., & Li, Z. (2024). Utilizing Deep Learning for Crystal System Classification in Lithium - Ion Batteries. Journal of Theory and Practice of Engineering Science, 4(03), 199–206. https://doi.org/10.53469/jtpes.2024.04(03).19.

Huang, S., Liang, Y., Shen, F., & Gao, F. (2024, July). Research on Federated Learning's Contribution to Trustworthy and Responsible Artificial Intelligence. In Proceedings of the 2024 3rd International Symposium on Robotics, Artificial Intelligence and Information Engineering (pp. 125-129).

Tang, Y., Zhao, S., & Yanjun, C. (2024). Regional Housing Supply and Demand Imbalance Qualitative Analysis in US based on Big Data.

Peng, Q., Planche, B., Gao, Z., Zheng, M., Choudhuri, A., Chen, T., ... & Wu, Z. (2024). 3d vision-language gaussian splatting. arXiv preprint arXiv:2410.07577.

Downloads

Published

2025-02-19

How to Cite

Si, Q. (2025). Application of Computer Big Data Technology in Urban Power Saving. Journal of Theory and Practice in Sciences, 2, 1–4. Retrieved from https://woodyinternational.com/index.php/jtps/article/view/160