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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Minkyung Kim & Sangdon Park & Joohyung Lee & Yongjae Joo & Jun Kyun Choi, 2017. "Learning-Based Adaptive Imputation Methodwith kNN Algorithm for Missing Power Data," Energies, MDPI, vol. 10(10), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1668-:d:115875
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    References listed on IDEAS

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    1. Chen, Wen & Zhou, Kaile & Yang, Shanlin & Wu, Cheng, 2017. "Data quality of electricity consumption data in a smart grid environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 98-105.
    2. Michael E. Tipping & Christopher M. Bishop, 1999. "Probabilistic Principal Component Analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 611-622.
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    Cited by:

    1. Kim, Jangkyum & Oh, Hyeontaek & Choi, Jun Kyun, 2022. "Learning based cost optimal energy management model for campus microgrid systems," Applied Energy, Elsevier, vol. 311(C).
    2. Soyeong Park & Seungwook Yoon & Byungtak Lee & Seokkap Ko & Euiseok Hwang, 2020. "Probabilistic Forecasting Based Joint Detection and Imputation of Clustered Bad Data in Residential Electricity Loads," Energies, MDPI, vol. 14(1), pages 1-13, December.
    3. Mitra, Somalee & Chakraborty, Basab & Mitra, Pabitra, 2024. "Smart meter data analytics applications for secure, reliable and robust grid system: Survey and future directions," Energy, Elsevier, vol. 289(C).

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