Abstract
With the development of smart grids and the widespread application of advanced measurement systems, power companies have obtained super-large power data, and the use of these data will bring huge benefits to the development of the power grid. Therefore, this paper analyzes the development status of electric power big data and the processing methods of data analysis, proposes a smart meter anomaly detection framework, and explains the advantages of the detection algorithm to provide a data mining solution for the development of electric power big data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bao, J.R., Jin, Y., Xiong, L.: Discussion on the method and application of electric energy big data collection based on big data cloud platform. China New Commun. 23(14), 101–102 (2021)
Jin, W.: Application of smart meter in smart grid. Electrotechnical (18), 53–54+62 (2020)
Luan, W.P., Yu, Y.X., Wang, B.: AMI data analysis method. Chin. Soc. Electr. Eng. 35(1), 29–36 (2015)
Yao, J.C., Liu, G.Z.: Bitrate-based no-reference video quality assessment combining the visual perception of video contents. IEEE Trans. Broadcast. 65(3), 546–557 (2019)
Liu, X.H., Gao, J.C., Wang, S.L., et al.: Smart grid abnormal power consumption detection frame-work. Autom. Technol. Appl. 39(12), 124–128+160 (2020)
Yao, J.C., Shen, J.: Image quality assessment based on the image contents visual perception. J. Electr. Imaging 30(5), 053024 (2021)
Liu, Y., Zhang, J.Y., Fan, H.Y., Lei, S.B., et al.: Analysis and diagnosis of remote error of smart meter based on electric power big data. Single Chip Microcomput. Embed. Syst. Appl. 21(09), 46–49+54 (2021)
Yao, J.C., Shen, J.: Objective assessment of image quality based on image content contrast perception. Acta Physica Sinica 69(14), 148702 (2020)
Yao, J.C., Liu, G.Z.: Improved SSIM IQA of contrast distortion based on the contrast sensitivity characteristics of HVS. IET Image Proc. 12(6), 872–879 (2018)
Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 61301237), the Natural Science Foundation of Jiangsu Province, China (No. BK20201468) and the Scientific Research Foundation for Advanced Talents, Nanjing Institute of Technology (No. YKJ201981).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Tang, H., Shen, J., Yao, C., Yao, J. (2022). Research on Anomaly Detection of Smart Meter Based on Big Data Mining. In: Tian, Y., Ma, T., Khan, M.K., Sheng, V.S., Pan, Z. (eds) Big Data and Security. ICBDS 2021. Communications in Computer and Information Science, vol 1563. Springer, Singapore. https://doi.org/10.1007/978-981-19-0852-1_29
Download citation
DOI: https://doi.org/10.1007/978-981-19-0852-1_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-0851-4
Online ISBN: 978-981-19-0852-1
eBook Packages: Computer ScienceComputer Science (R0)