Remote Identification for Smart-meter Operation Error based on a Wireless Sensor Network in a Smart City

被引：0
作者：
Liu W. [1 ]
Zhou H. [1 ]
Zhang M. [1 ]
Shang Y. [1 ]
Liu Y. [1 ]
机构：
[1] State Grid Liaoning Marketing Service Center, Liaoning, Shenyang
来源：
IEIE Transactions on Smart Processing and Computing | 2022年 / 11卷 / 06期
关键词：
Operation error; Remote identification; Sensor network; Smart city; Smart meter; Wireless;
D O I：
10.5573/IEIESPC.2022.11.6.444
中图分类号：
学科分类号：
摘要：
This paper presents a remote identification method for smart-meter operation errors based on a wireless sensor network (WSN) in a smart city to reduce the bad operation of smart meters. In the WSN, ZigBee communication technology was used to collect data from smart meters to enhance the reliability of information. The LEACH protocol can effectively extend the network life cycle and was selected as a communication protocol for WSNs to collect operation data from intelligent meters. Based on the operation data of a smart meter collected by a WSN, a remote identification model of operation error of a smart meter was established. The limited memory least square algorithm was adopted to construct the remote identification model of operation error for a smart meter to realize the remote identification of a smart-meter error in a smart city. The proposed method can use a WSN to collect operation data from a smart meter and remotely identify the mean absolute percentage error (MAPE) and root mean square error (RMSE) of a smart meter's operation error, which were less than 1%. Copyrights © 2022 The Institute of Electronics and Information Engineers.
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页码：444 / 454
页数：10
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共 25 条
  • [1] Cheng L., Xue M., Wang Y., Bi Y, A robust tracking algorithm based on modified generalized probability data as-sociation for wireless sensor network, IEEE Transactions on Industrial Electronics, pp. 1-8, (2021)
  • [2] Priyanka E. B., Devi T. K, Sakthivel P., Sagayaraj A. S., Gate diffusion input (gdi) codes involved viterbi decoders in wireless sensor network for enhancing qos service, Analog Integrated Circuits and Signal Processing, 111, 2, pp. 287-298, (2022)
  • [3] Nancy P., Muthurajkumar S., Ganapathy S., Kumar S., Selvi M., Arputharaj K., Intrusion detection using dynamic feature selection and fuzzy temporal decision tree classification for wireless sensor network s, IET Communications, 14, 5, pp. 888-895, (2020)
  • [4] Qiu S., Zhu Y. H., Tian X., Chi K., Goodputmaximised data delivery scheme for battery-free wireless sensor network, IET Communications, 14, 4, pp. 665-673, (2020)
  • [5] Abbasikesbi R., Nikfarjam A., Nemati M., Developed wireless sensor network to supervise the essential parameters in greenhouses for internet of things applications, IET Circuits Devices & Systems, 14, 8, pp. 1258-1264, (2020)
  • [6] Wang Y., Li Y. R., Simulation of Clustering Scheduling Method for Multi-Concurrent Tasks in Internet of Things, Computer Simulation, 37, 3, pp. 475-479, (2020)
  • [7] Liu F., Liang C., He Q., Remote malfunctional smart meter detection in edge computing environment, IEEE Access, 99, pp. 1-7, (2020)
  • [8] Taso Y. C., Ho C. W., Chen R. S., The impact of problem awareness and biospheric values on the intention to use a smart meter, Energy Policy, 147, 1, (2020)
  • [9] Yusoff N. S., Kaman Z. K., Zahari A. R., Wan H., Abdullah A. B., Examining smart meter users' experience on continuance intention in adopting smart meter in malaysia - result from a pilot study, Asia Proceedings of Social Sciences, 7, 2, pp. 110-113, (2021)
  • [10] Wang Q., Dai Y., Wu Y., Liu Y., Wang T., Dong X., Data model for smart electricity meter comprehensive verification based on bp neural network, Journal of Physics: Conference Series, 1486, 2, (2020)
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