KEPCO Journal on Electric Power and Energy

  • 제7권2호
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  • Pages.301-307
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  • 2021
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  • 2465-8111(pISSN)
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  • 2466-0124(eISSN)
한국전력공사 (Korea Electric Power Corporation)
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머신러닝 기법을 활용한 에너지 데이터 분석에 관한 연구

A Research on the Energy Data Analysis using Machine Learning

  • Kim, Dongjoo (KEPCO Research Institute, Korea Electric Power Corporation) ;
  • Kwon, Seongchul (KEPCO Research Institute, Korea Electric Power Corporation) ;
  • Moon, Jonghui (KEPCO Research Institute, Korea Electric Power Corporation) ;
  • Sim, Gido (KEPCO Research Institute, Korea Electric Power Corporation) ;
  • Bae, Moonsung (KEPCO Research Institute, Korea Electric Power Corporation)
  • 투고 : 2021.07.12
  • 심사 : 2021.09.27
  • 발행 : 2021.12.30
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초록

After the spread of the data collection devices such as smart meters, energy data is increasingly collected in a variety of ways, and its importance continues to grow. However, due to technical or practical limitations, errors such as missing or outliers in the data occur during data collection process. Especially in the case of customer-related data, billing problems may occur, so energy companies are conducting various research to process such data. In addition, efforts are being made to create added value from data, which makes it difficult to provide such services unless reliability of data is guaranteed. In order to solve these challenges, this research analyzes prior research related to bad data processing specifically in the energy field, and propose new missing value processing methods to improve the reliability and field utilization of energy data.

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과제정보

본 연구는 국토교통부 국토교통과학기술진흥원의 스마트시티 혁신성자동력 프로젝트 지원으로 수행되었음 (과제번호 21NSPS-B149869-04)

참고문헌

  1. U.S. Energy Information Agency, "FAQ: How many smart meters are installed in the United States, and who has them? ", Available: http://www.eia.gov/tools/faqs/faq.php?id=108&t=3
  2. "Smart Meters, Quarterly Report to end December 2017", Department for Business Energy and Industrial Strategy, Great Britain, Technical Report, 2018.
  3. "제2차 지능형전력망 기본계획", 산업통상자원부, 2018.
  4. Y. Wang, Q. Chen, T. Hong, C. Kang, "Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges," IEEE Trans. Smart Grid, vol. PP, no. 99, pp. 1-1, 2018.
  5. J. Peppanen, X. Zhang, S. Grijalva, and M. J. Reno, "Handling bad or missing smart meter data through advanced data imputation," in IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), pp. 1-5, 2016.
  6. A. Al-Wakeel, J. Wu, and N. Jenkins, "k-means based load estimation of domestic smart meter measurements," Applied Energy, vol. 194, pp. 333-342, 2017. https://doi.org/10.1016/j.apenergy.2016.06.046
  7. D. B. Araya, K. Grolinger, H. F. ElYamany, M. A. Capretz, and G. Bitsuamlak, "An ensemble learning framework for anomaly detection in building energy consumption," Energy and Buildings, vol. 144, pp. 191-206, 2017. https://doi.org/10.1016/j.enbuild.2017.02.058