Learning-based adaptive imputation method with kNN algorithm for missing power data = kNN 알고리즘을 기반으로 학습 기법을 도입한 누락된 전력 데이터 추정법에 관한 연구

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We address the imputation of missing power consumption data in AMI. As the power consumption data are collected from more various power consumers, we propose a method to improve imputation accuracy by improving limitations of existing methods. In detail, we propose a method selection that takes into account the variability of the missing situation. Based on past similar situations, the kNN classification algorithm is used to select a more appropriate imputation method between the linear interpolation method and the historical average method. Next, we propose a method to select historical data useful for imputation and improve the existing historical average method based on kNN regression algorithm. Finally, it is shown through actual measured power data that the imputation accuracy is improved by applying the proposed method.
Advisors
Choi, Junkyunresearcher최준균researcher
Description
한국과학기술원 :전기및전자공학부,
Publisher
한국과학기술원
Issue Date
2018
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2018.2,[iii, 32 p. :]

Keywords

Missing data imputation▼apower consumption data▼akNN algorithm▼asmart meter▼aadvanced metering infrastructure; 누락 데이터 추정▼a전력 소비 데이터▼akNN 알고리즘▼a스마트 미터▼a지능형전략계량인프라

URI
http://hdl.handle.net/10203/266977
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=733989&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
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