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Cluster-Based Prediction for Batteries in Data Centers

Author

Listed:
  • Syed Naeem Haider

    (Center for Intelligent and Networked Systems (CFINS), Department of Automation and BNRist, Tsinghua University, Beijing 100084, China)

  • Qianchuan Zhao

    (Center for Intelligent and Networked Systems (CFINS), Department of Automation and BNRist, Tsinghua University, Beijing 100084, China)

  • Xueliang Li

    (Center for Intelligent and Networked Systems (CFINS), Department of Automation and BNRist, Tsinghua University, Beijing 100084, China)

Abstract

Prediction of a battery’s health in data centers plays a significant role in Battery Management Systems (BMS). Data centers use thousands of batteries, and their lifespan ultimately decreases over time. Predicting battery’s degradation status is very critical, even before the first failure is encountered during its discharge cycle, which also turns out to be a very difficult task in real life. Therefore, a framework to improve Auto-Regressive Integrated Moving Average (ARIMA) accuracy for forecasting battery’s health with clustered predictors is proposed. Clustering approaches, such as Dynamic Time Warping (DTW) or k-shape-based, are beneficial to find patterns in data sets with multiple time series. The aspect of large number of batteries in a data center is used to cluster the voltage patterns, which are further utilized to improve the accuracy of the ARIMA model. Our proposed work shows that the forecasting accuracy of the ARIMA model is significantly improved by applying the results of the clustered predictor for batteries in a real data center. This paper presents the actual historical data of 40 batteries of the large-scale data center for one whole year to validate the effectiveness of the proposed methodology.

Suggested Citation

  • Syed Naeem Haider & Qianchuan Zhao & Xueliang Li, 2020. "Cluster-Based Prediction for Batteries in Data Centers," Energies, MDPI, vol. 13(5), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1085-:d:326957
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    References listed on IDEAS

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    Cited by:

    1. Denis Sidorov & Fang Liu & Yonghui Sun, 2020. "Machine Learning for Energy Systems," Energies, MDPI, vol. 13(18), pages 1-6, September.

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