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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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    1. You, Gae-won & Park, Sangdo & Oh, Dukjin, 2016. "Real-time state-of-health estimation for electric vehicle batteries: A data-driven approach," Applied Energy, Elsevier, vol. 176(C), pages 92-103.
    2. Jiang, Li & Li, Yong & Huang, Yuduo & Yu, Jiaqi & Qiao, Xuebo & Wang, Yixiao & Huang, Chun & Cao, Yijia, 2020. "Optimization of multi-stage constant current charging pattern based on Taguchi method for Li-Ion battery," Applied Energy, Elsevier, vol. 259(C).
    3. Luping Chen & Liangjun Xu & Yilin Zhou, 2018. "Novel Approach for Lithium-Ion Battery On-Line Remaining Useful Life Prediction Based on Permutation Entropy," Energies, MDPI, vol. 11(4), pages 1-15, April.
    4. Kavasseri, Rajesh G. & Seetharaman, Krithika, 2009. "Day-ahead wind speed forecasting using f-ARIMA models," Renewable Energy, Elsevier, vol. 34(5), pages 1388-1393.
    5. Gustavo Callou & João Ferreira & Paulo Maciel & Dietmar Tutsch & Rafael Souza, 2014. "An Integrated Modeling Approach to Evaluate and Optimize Data Center Sustainability, Dependability and Cost," Energies, MDPI, vol. 7(1), pages 1-40, January.
    6. Joao Ferreira & Gustavo Callou & Dietmar Tutsch & Paulo Maciel, 2018. "PLDAD—An Algorihm to Reduce Data Center Energy Consumption," Energies, MDPI, vol. 11(10), pages 1-24, October.
    7. Lingling Li & Pengchong Wang & Kuei-Hsiang Chao & Yatong Zhou & Yang Xie, 2016. "Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Gaussian Processes Mixture," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-13, September.
    8. João Ferreira & Gustavo Callou & Paulo Maciel, 2013. "A Power Load Distribution Algorithm to Optimize Data Center Electrical Flow," Energies, MDPI, vol. 6(7), pages 1-22, July.
    9. Matyjaszek, Marta & Riesgo Fernández, Pedro & Krzemień, Alicja & Wodarski, Krzysztof & Fidalgo Valverde, Gregorio, 2019. "Forecasting coking coal prices by means of ARIMA models and neural networks, considering the transgenic time series theory," Resources Policy, Elsevier, vol. 61(C), pages 283-292.
    10. Xiaopeng Tang & Ke Yao & Boyang Liu & Wengui Hu & Furong Gao, 2018. "Long-Term Battery Voltage, Power, and Surface Temperature Prediction Using a Model-Based Extreme Learning Machine," Energies, MDPI, vol. 11(1), pages 1-16, January.
    11. Huan Wang & Jiejun Huang & Han Zhou & Lixue Zhao & Yanbin Yuan, 2019. "An Integrated Variational Mode Decomposition and ARIMA Model to Forecast Air Temperature," Sustainability, MDPI, vol. 11(15), pages 1-11, July.
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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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