Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Machine Learning-based Power Usage Abnormality Detection

  • Han-Sung Lee (Dept. of Computer Education, Andong National University) ;
  • Young-Bok Cho (Dept. of Computer Education, Andong National University)
  • Received : 2024.11.05
  • Accepted : 2024.11.24
  • Published : 2024.11.29
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Abstract

In this paper, we propose a method to detect abnormal power usage conditions in domestic franchise convenience stores, by detecting cases where the temperature of the refrigeration or freezer equipment operates outside the normal range and classifying detailed abnormal situations. Compared to normal data, abnormal data is very small, and the amount of data varies depending on the type of abnormality, leading to a data imbalance issue. The proposed method employs a hierarchical structure that combines a time series classification algorithm with kNN, addressing the data imbalance problem and enabling classification using relatively small amounts of data. In this paper, we conducted an experiment by independently constructing our own dataset to validate the proposed methodology.

본 논문에서는 연구에서는 국내 프랜차이즈 가맹 편의점의 전력 사용 이상 상태를 탐지하기 위하여, 편의점의 냉장 또는 냉동 장비의 온도가 비정상 범위로 가동되는 경우를 탐지하고, 세부적인 비정상 상황을 분류하는 방법을 제안한다. 정상 데이터에 비하여 비정상 데이터는 매우 적으며, 비정상 유형에 따라 데이터의 분량이 서로 달라 데이터 불균형 문제를 내포하고 있다. 제안하는 방법은 시계열 분류 알고리즘과 kNN을 결합한 계층적 구조로 되어 있으며, 데이터 불균형 문제를 해결하고 비교적 적은 데이터를 이용하여 분류 문제를 해결할 수 있다. 본 논문에서는 제안된 방법론을 검증하기 위하여 자체적으로 데이터 집합을 구성하여 실험을 수행하였다.

Keywords

Acknowledgement

This work was supported by a Research Grant of Andong National University

References

  1. Korea Law Information Center (2024), Energy Use Rationalization Act, [available] https://www.law.go.kr/LSW/lsInfoP.do?lsId=001867&ancYnChk=0#0000 
  2. J. Sn, D. Kim, and S. Jo, "Design of Gateway for Data Linkage Between Power System", Proceeding of the Korea Computer Congress 2023. pp. 1451-1453. Jeju Korea. 
  3. S. I Hong, and C. H. Lim, "A Multi-Protocol Gateway Middleware Design on Ubiquitous Sensor Networks." The Journal of Korean Institute of Communications and Information Sciences, Vol38, No.11, pp.895-901. Nomember. 2013. DOI: 10.7840/kics.2013.38B.11.895 
  4. Y. Yu, Si, X., C.. Hu, and J. Zhang, J. "A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures." Neural Computation, Vol.31, No. 7, pp. 1235-1270. June 2019. DOI: 10.1162/neco_a_01199 
  5. K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink, B. R. and J. Schmidhuber. "LSTM: A Search Space Odyssey." IEEE Transactions on Neural Networks and Learning Systems, Vol.28. No.10, pp. 2222-2232. July 2016. DOI: 10.1109/TNNLS.2016.2582924 
  6. F. A. Gers, J. Schmidhuber, and F. Cummins. "Learning to Forget: Continual Prediction with LSTM." Neural Computation, Vol.12. No.10, pp. 2541-2471. January, 2000. DOI:10.1162/089976600300015015 
  7. R. Dey and F. M. Salem "Gate-variants of Gated Recurrent Unit (GRU) neural networks." Proceedings of the IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), Vol. 30. No.1, pp. 1597-1600. Boston, MA, USA, 2017. DOI: 10.1109/MWSCAS.2017.8053243 
  8. Y. B. Cho. "Gate-variants of Gated Recurrent Unit (GRU) neural networks." Journal of the Korea Society of Computer and Information, Vol. 29, No.9 pp.61-68, September 2024. DOI : 10.9708/jksci.2024.29.09.061 
  9. N. Elsayed, A. S. Maida and M. Bayoumi, "Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification." International Journal of Advanced Computer Science and Applications (IJACSA), Vol.10. No.5, pp. 654-664. February 2019. DOI: 1109/ACCESS.2019.2936516  1109/ACCESS.2019.2936516
  10. J. W. Kang, S. H. Han, J. H. Park and K. J. Kim "Development of Machine Learning-based Flood Depth and Location Prediction Model." The Journal of the Korea Institute of Electronic Communication Sciences, Vol.18. No.1, pp. 91-98. February 2023. DOI : 10.13067/JKIECS.2023.18.1.91 
  11. H. S. Lee, Y. H. Jeong, and S. H. Jung. "Intrusion Detection Approach using Feature Learning and Hierarchical Classification." The Journal of the Korea Institute of Electronic Communication Sciences, Vol.19. No.1, pp. 249-256. February 2024. DOI: 10.13067/JKIECS.2024.19.1.249 
  12. J. W. Kwon and S. Y. Cho "Performance Analysis of Fingerprinting Method for LTE Positioning according to W-KNN Correlation Techniques in Urban Area." The Journal of the the Korea Institute of Electronic Communication Sciences, Vol.16. No.6, pp.1059-1068. December 2021. DOI: 10.13067/JKIECS.2021.16.6.1059