Learning-Based Adaptive Imputation Methodwith kNN Algorithm for Missing Power Data

Minkyung Kim, Sangdon Park, Joohyung Lee, Yongjae Joo and Jun Kyun Choi
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Minkyung Kim: Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea
Sangdon Park: Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea
Joohyung Lee: Department of Software, Gachon University, Seongnam 13120, Korea
Yongjae Joo: Korea Electric Power Research Institute, Daejeon 305-760, Korea
Jun Kyun Choi: Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea

Energies, 2017, vol. 10, issue 10, 1-20

Abstract: This paper proposes a learning-based adaptive imputation method (LAI) for imputing missing power data in an energy system. This method estimates the missing power data by using the pattern that appears in the collected data. Here, in order to capture the patterns from past power data, we newly model a feature vector by using past data and its variations. The proposed LAI then learns the optimal length of the feature vector and the optimal historical length, which are significant hyper parameters of the proposed method, by utilizing intentional missing data. Based on a weighted distance between feature vectors representing a missing situation and past situation, missing power data are estimated by referring to the k most similar past situations in the optimal historical length. We further extend the proposed LAI to alleviate the effect of unexpected variation in power data and refer to this new approach as the extended LAI method (eLAI). The eLAI selects a method between linear interpolation (LI) and the proposed LAI to improve accuracy under unexpected variations. Finally, from a simulation under various energy consumption profiles, we verify that the proposed eLAI achieves about a 74% reduction of the average imputation error in an energy system, compared to the existing imputation methods.

Keywords: missing data; power data; imputation; kNN algorithm; learning; smart meter; energy system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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