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http://dx.doi.org/10.18770/KEPCO.2021.07.02.301

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)
Publication Information
KEPCO Journal on Electric Power and Energy / v.7, no.2, 2021 , pp. 301-307 More about this Journal
Abstract
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.
Keywords
Bad Data Management; Machine Learning; Energy Big Data;
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  • Reference
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