IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i10p1668-d115875.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/10/1668/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/10/1668/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhu Liu & Lingfeng Xuan & Dehuang Gong & Xinlin Xie & Zhongwen Liang & Dongguo Zhou, 2025. "A WGAN-GP Approach for Data Imputation in Photovoltaic Power Prediction," Energies, MDPI, vol. 18(5), pages 1-16, February.
    2. 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).
    3. 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.
    4. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xin Xu & Yang Lu & Yupeng Zhou & Zhiguo Fu & Yanjie Fu & Minghao Yin, 2021. "An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks," Mathematics, MDPI, vol. 9(15), pages 1-14, July.
    2. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Papers 2310.17278, arXiv.org, revised Jan 2024.
    3. Chen, Andrew Y. & McCoy, Jack, 2024. "Missing values handling for machine learning portfolios," Journal of Financial Economics, Elsevier, vol. 155(C).
    4. Wang, Shao-Hsuan & Huang, Su-Yun, 2022. "Perturbation theory for cross data matrix-based PCA," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    5. Wentao Qu & Xianchao Xiu & Huangyue Chen & Lingchen Kong, 2023. "A Survey on High-Dimensional Subspace Clustering," Mathematics, MDPI, vol. 11(2), pages 1-39, January.
    6. Jiaju Miao & Pawel Polak, 2023. "Online Ensemble of Models for Optimal Predictive Performance with Applications to Sector Rotation Strategy," Papers 2304.09947, arXiv.org.
    7. Jingying Yang, 2024. "Element Aggregation for Estimation of High-Dimensional Covariance Matrices," Mathematics, MDPI, vol. 12(7), pages 1-16, March.
    8. Dorota Toczydlowska & Gareth W. Peters, 2018. "Financial Big Data Solutions for State Space Panel Regression in Interest Rate Dynamics," Econometrics, MDPI, vol. 6(3), pages 1-45, July.
    9. Jung, WoongHee & Taflanidis, Alexandros A., 2023. "Efficient global sensitivity analysis for high-dimensional outputs combining data-driven probability models and dimensionality reduction," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    10. Matteo Barigozzi, 2023. "Asymptotic equivalence of Principal Components and Quasi Maximum Likelihood estimators in Large Approximate Factor Models," Papers 2307.09864, arXiv.org, revised Jun 2024.
    11. Arthur Pewsey & Eduardo García-Portugués, 2021. "Recent advances in directional statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 1-58, March.
    12. Johannes Burge & Priyank Jaini, 2017. "Accuracy Maximization Analysis for Sensory-Perceptual Tasks: Computational Improvements, Filter Robustness, and Coding Advantages for Scaled Additive Noise," PLOS Computational Biology, Public Library of Science, vol. 13(2), pages 1-32, February.
    13. Tilman M. Davies & Sudipto Banerjee & Adam P. Martin & Rose E. Turnbull, 2022. "A nearest‐neighbour Gaussian process spatial factor model for censored, multi‐depth geochemical data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(4), pages 1014-1043, August.
    14. el Bouhaddani, Said & Uh, Hae-Won & Hayward, Caroline & Jongbloed, Geurt & Houwing-Duistermaat, Jeanine, 2018. "Probabilistic partial least squares model: Identifiability, estimation and application," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 331-346.
    15. Stefan Sommer, 2019. "An Infinitesimal Probabilistic Model for Principal Component Analysis of Manifold Valued Data," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 37-62, February.
    16. Seppo Pulkkinen & Marko Mäkelä & Napsu Karmitsa, 2014. "A generative model and a generalized trust region Newton method for noise reduction," Computational Optimization and Applications, Springer, vol. 57(1), pages 129-165, January.
    17. Gen Li & Sungkyu Jung, 2017. "Incorporating covariates into integrated factor analysis of multi‐view data," Biometrics, The International Biometric Society, vol. 73(4), pages 1433-1442, December.
    18. Jeff Goldsmith & Vadim Zipunnikov & Jennifer Schrack, 2015. "Generalized multilevel function-on-scalar regression and principal component analysis," Biometrics, The International Biometric Society, vol. 71(2), pages 344-353, June.
    19. Ibrahim Alotaibi & Mohammed A. Abido & Muhammad Khalid & Andrey V. Savkin, 2020. "A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources," Energies, MDPI, vol. 13(23), pages 1-41, November.
    20. Frydman, Carola & Papanikolaou, Dimitris, 2018. "In search of ideas: Technological innovation and executive pay inequality," Journal of Financial Economics, Elsevier, vol. 130(1), pages 1-24.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1668-:d:115875. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.