A Research on the Energy Data Analysis using Machine Learning
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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) |
1 | "Smart Meters, Quarterly Report to end December 2017", Department for Business Energy and Industrial Strategy, Great Britain, Technical Report, 2018. |
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3 | 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. DOI |
4 | 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 |
5 | 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. DOI |
6 | 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. |
7 | 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. |
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