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A Data Imputation Approach for Missing Power Consumption Measurements in Water-Cooled Centrifugal Chillers

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  • Sung Won Kim

    (Department of Architectural Engineering, Graduate School, Seoul National University of Science & Technology, Seoul 01811, Republic of Korea)

  • Young Il Kim

    (School of Architectural, Seoul National University of Science & Technology, Seoul 01811, Republic of Korea)

Abstract

In the process of collecting operational data for the performance analysis of water-cooled centrifugal chillers, missing values are inevitable due to various factors such as sensor errors, data transmission failures, and failure of the measurement system. When a substantial amount of missing data is present, the reliability of data analysis decreases, leading to potential distortions in the results. To address this issue, it is necessary to either minimize missing occurrences by utilizing high-precision measurement equipment or apply reliable imputation techniques to compensate for missing values. This study focuses on two water-cooled turbo chillers installed in Tower A, Seoul, collecting a total of 118,464 data points over 3 years and 4 months. The dataset includes chilled water inlet and outlet temperatures ( T 1 and T 2 ) and flow rate ( V ˙ 1 ) and cooling water inlet and outlet temperatures ( T 3 and T 4 ) and flow rate ( V ˙ 3 ), as well as chiller power consumption ( W ˙ c ). To evaluate the performance of various imputation techniques, we introduced missing values at a rate of 10–30% under the assumption of a missing-at-random (MAR) mechanism. Seven different imputation methods—mean, median, linear interpolation, multiple imputation, simple random imputation, k-nearest neighbors (KNN), and the dynamically clustered KNN (DC-KNN)—were applied, and their imputation performance was validated using MAPE and CVRMSE metrics. The DC-KNN method, developed in this study, improves upon conventional KNN imputation by integrating clustering and dynamic weighting mechanisms. The results indicate that DC-KNN achieved the highest predictive performance, with MAPE ranging from 9.74% to 10.30% and CVRMSE ranging from 12.19% to 13.43%. Finally, for the missing data recorded in July 2023, we applied the most effective DC-KNN method to generate imputed values that reflect the characteristics of the studied site, which employs an ice thermal energy storage system.

Suggested Citation

  • Sung Won Kim & Young Il Kim, 2025. "A Data Imputation Approach for Missing Power Consumption Measurements in Water-Cooled Centrifugal Chillers," Energies, MDPI, vol. 18(11), pages 1-37, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:11:p:2779-:d:1665306
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    References listed on IDEAS

    as
    1. Sung Won Kim & Young Il Kim, 2025. "Performance Prediction of a Water-Cooled Centrifugal Chiller in Standard Temperature Conditions Using In-Situ Measurement Data," Sustainability, MDPI, vol. 17(5), pages 1-39, March.
    2. Jong-Won Lee & Young Il Kim, 2020. "Energy Saving of a University Building Using a Motion Detection Sensor and Room Management System," Sustainability, MDPI, vol. 12(22), pages 1-17, November.
    3. Wang, Yijun & Jin, Xinqiao & Shi, Wantao & Wang, Jiangqing, 2019. "Online chiller loading strategy based on the near-optimal performance map for energy conservation," Applied Energy, Elsevier, vol. 238(C), pages 1444-1451.
    4. Christophe Crambes & Chayma Daayeb & Ali Gannoun & Yousri Henchiri, 2025. "Multiple imputation in the functional linear model with partially observed covariate and missing values in the response," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 54(1), pages 49-69, January.
    5. Lee, Tzong-Shing & Lu, Wan-Chen, 2010. "An evaluation of empirically-based models for predicting energy performance of vapor-compression water chillers," Applied Energy, Elsevier, vol. 87(11), pages 3486-3493, November.
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