Abstract
The automation rapid development of the power distribution network have not been fully utilized with the terminal coverage rate increment. The demand and complexity of the power distribution network applications are also fast updated leading a huge calculation pressure from cloud service. This paper does data mining from power distribution network in three aspects, including delay, complexity and power. It defines them with respective weights according to the application requirements, and propose a cloud-edge collaborative communication scheme to effectively reduce the computing complexity of the system.
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References
Zheng, L., et al.: Research on power demand forecasting based on the relationship Between economic development and power demand. In: 2018 China International Conference on Electricity Distribution (CICED), pp. 2710–2713, IEEE (2018)
Chen, W., Guo, M., Jin, Q., Yao, Z.: Reliability analysis method of power quality monitoring device based on non-parametric estimation. In: 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 1526–1529. IEEE (2019)
Zhou, Z., et al.: Validity evaluation method of DGA monitoring sensor in power transformer based on chaos theory. In: 2018 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP), pp. 402–405. IEEE (2018)
Choi, W., Lee, W., Sarlioglu, B.: Reactive power control of grid-connected inverter in vehicle-to-grid application for voltage regulation. In: 2016 IEEE Transportation Electrification Conference and Expo (ITEC), pp. 1–7. IEEE (2016)
Cai, A., Yu, Y., Xu, L., Niu, Y., Yan, J.: Review on reactive power compensation of electric vehicle charging piles. In: 2019 22nd International Conference on Electrical Machines and Systems (ICEMS), pp. 1–4. IEEE (2019)
Yuan, W., Wang, C., Lei, X., Li, Q., Shi, Z., Yu, Y.: Multi-area scheduling model and strategy for power systems with large-scale new energy and energy storage. In: 2018 Chinese Automation Congress (CAC), pp. 2419–2424. IEEE (2018)
Mao, M., Humphrey, M.: A performance study on the VM startup time in the cloud. In: 2012 IEEE Fifth International Conference on Cloud Computing, pp. 423–430. IEEE (2012)
Shi, W., Sun, H., Cao, J.: Edge computing-an emerging computing model for the internet of everything era. J. Comput. Res. Develop. 54(5), 129–133 (2017)
Xue, M., Shi, K., Chen, X., Wu, Q., Li, B., Qi, B.: A summary of research on converged communication model of power network and information network. In: 2017 2nd International Conference on Power and Renewable Energy (ICPRE), pp. 1062–1066. IEEE (2017)
Zhu, W., Qiang, F., Liu, N., Wu, X., Li, K., Zhang, K.: Research on multi-node collaborative computing management model for distribution Internet of Things. In: International Conference on Artificial Intelligence and Computer Applications, pp. 1019–1022. IEEE (2020)
Fan, C., Lu, Y., Leng, X., Luan, W., Gu, J., Yang, W.: Data classification processing method for the power IoT based on cloud-side collaborative architecture. In: IEEE 9th Joint International Information Technology and Artificial Intelligence Conference, pp. 684–687. IEEE (2020)
Chen, X.J., Li, Z., Bai, B.M., Cai, J.P.: A certain chaotic pseudo-random sequence complexity entropy measure of fuzzy relations. 60(06), 379–388 (2011)
Wen, B.H., Yuan, M., Hou, L.: The study of CSI 300 index’s complexity and comparison of model efficiency based on entropy algorithm. J. Quant. Econ. 32(1), 19–25 (2015)
Zhang, W., Wen, Y., Guan, K., Kilper, D., Luo, H., Wu, D.O.: Energy-optimal mobile cloud computing under stochastic wireless channel. IEEE Trans. Wireless Commun. 12(9), 4569–4581(2013)
Acknowledgment
This research work is supported by the National Key Research and Development Program of China (2021YFE0105500); and the National Natural Science Foundation of China (61801166).
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An, L., Su, X. (2022). Cloud-Edge Collaboration Based Data Mining for Power Distribution Networks. In: Gao, H., Wun, J., Yin, J., Shen, F., Shen, Y., Yu, J. (eds) Communications and Networking. ChinaCom 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 433. Springer, Cham. https://doi.org/10.1007/978-3-030-99200-2_33
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DOI: https://doi.org/10.1007/978-3-030-99200-2_33
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