Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 25, 20 December 2023
Open
Access | Article
Smart meter failure prediction based on weakened grey markov model with IOWA operator
* Author to whom correspondence should be addressed.
Abstract
Scientifically predicting the annual failure quantity of smart meters is of significant importance for enhancing the economic benefits of smart meters and promoting the stable operation of smart grids. In this paper, traditional Grey Markov prediction models and Grey Markov models with weakened buffering operators are employed to predict smart meter failure data. To improve prediction accuracy, an Induced Ordered Weighted Averaging (IOWA) operator is introduced to construct a combination prediction model. Based on this approach, we predict the annual failure quantity of smart meters for a certain company in Wuhan, China, from 2020 to 2022 using data from 2012 to 2019. Accuracy indicators, such as correlation degree (G) and average relative error (P), have improved from level three to level two, indicating that the combination prediction model based on the IOWA operator effectively enhances prediction accuracy. This method demonstrates the feasibility and effectiveness of predicting smart meter failures.
Keywords
Smart meter, combination prediction, Grey prediction, IOWA operator
References
1. Liu Kaixin,Qi Jia,Zhou Zhen, et al.Multistate time lag dynamic Bayesian networks model for reliability prediction of smart meters [J] Microelectronics Reliability.Volume 138,Issue.2022.
2. Tao Peng,Shen Hongtao,Zhang Yangrui,et al.Research on the Life Prediction Method of Meters Based on a Nonlinear Wiener Process [J] Electronics.Volume 11 ,Issue 13.2022
3. Liang, Q. Y. (2023). Analysis and replacement management of intelligent electricity meter life prediction with the integration of electricity and environmental data [Doctoral dissertation, Dongbei University of Finance and Economics].
4. Huang, J. T., Fan, B., Zhou, Y. F., et al. (2019). Intelligent electricity meter fault and life prediction model based on random forest. Bing Gong Zi Dong Hua, 38(10), 57-60.
5. Wen, Y. K., Hou, H. J., Wang, Y. (2022). Intelligent electricity meter fault prediction based on Apriori and C5.0 algorithms. Zi Dong Hua Yi Biao, 43(05), 90-94+101.
6. Li, N., Zhang, W., Guo, Z. L., et al. (2022). Intelligent electricity meter fault prediction based on multi-class machine learning models. Dian Qi Yu Neng Xiao Guan Ji Shu, 2022(02), 6-11.
7. Gao, W. J., Xue, B. B., Pang, Z. J. (2022). Intelligent electricity meter fault prediction based on spatiotemporal convolutional neural networks. Dian Zi Ji Shu Ying Yong, 48(03), 59-63.
8. Fan, S. H. (2018). Research on intelligent electricity meter fault prediction technology based on big data analysis [Doctoral dissertation, Beijing University of Posts and Telecommunications].
9. Yang, J. C., Jiang, P., Chen, G. Y., et al. (2017). Research on intelligent electricity meter fault identification model. Dian Qi Yu Neng Xiao Guan Ji Shu, 2017(06), 30-36.
10. Jin Shuo Liu;Xin He;Shao Teng Li etc.Vertical Analysis Based on the Fault Data of Running Smart Meter.[J] Applied Mechanics and Materials.Volume 2370,Issue 313-314.
11. Liu, S. F. (2010). Grey system theory and its applications (5th ed.). Nanjing Hangkong Hangtian University.
12. Dang, Y. G., Liu, S. F., Liu, B., et al. (2004). Research on weakening buffer operator. Zhong Guo Guan Li Ke Xue, 2004(02), 109-112.
13. Huang, Y. S., Wang, F. F. (2013). Grey prediction based on weighted buffer operator. Dian Zi Ce Shi, 2013(20), 56-57.
14. Liu Sifeng, Deng Julong. The range suitable for GM (1,1)[J].Systems Engineering-Theory & Practice,2000, (5):121-124.
15. Ren, Y. J. (2023). Research on combination prediction model based on VIKOR-IOWA operator [Doctoral dissertation, Beijing Jiaotong University].
16. Zhang, Y. F. (2018). Combination prediction model based on IOWA operator under uncertain information [Doctoral dissertation, Anhui University].
17. Guo, L. J., Zhang, J. H. (2022). Wheat yield prediction based on combination of IOWA operator and grey neural network. Liang Shi Yu You Zhi, 35(10), 26-30.
18. Zhai, W. H. (2018). Improvement and application of grey Markov combination prediction model [Doctoral dissertation, Xi’an University of Architecture and Technology].
19. Wu, Z. M., Li, S. H., Wu, Y., et al. (2021). Consistency analysis of intelligent electricity meter accelerated degradation mechanism based on grey theory. Wuhan Da Xue Xue Bao (Gong Xue Ban), 54(11), 1070-1076.
20. Zhang, L. P., Hu, S. S., Mei, N., et al. (2020). A review of reliability research on intelligent energy meters. Dian Ce Yu Yi Biao, 57(16), 134-140.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).
- Volume Title
- Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
- ISBN (Print)
- 978-1-83558-233-6
- ISBN (Online)
- 978-1-83558-234-3
- Published Date
- 20 December 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/25/20240929
- Copyright
- 20 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited