Article
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Clustered Based Prediction for Batteries in the Data Centers
Syed Naeem Haider
Version 1
: Received: 31 January 2020 / Approved: 31 January 2020 / Online: 31 January 2020 (13:28:01 CET)
A peer-reviewed article of this Preprint also exists.
Haider, S.N.; Zhao, Q.; Li, X. Cluster-Based Prediction for Batteries in Data Centers. Energies 2020, 13, 1085. Haider, S.N.; Zhao, Q.; Li, X. Cluster-Based Prediction for Batteries in Data Centers. Energies 2020, 13, 1085.
Journal reference: Energies 2020, 13, 1085
DOI: 10.3390/en13051085
Abstract
This paper proposes an ARIMA approach to battery health forecasting with accuracy improvement by K shape-based clustered predictors. The health prediction of the battery pack is an important function of a battery management system in data centers. Accurate forecasting of battery life turns out to be very difficult without failure data to train a good forecasting model in real life. The conventional ARIMA model is compared with total and clustered predictors for battery health forecasting. Results show that the forecasting accuracy of the ARIMA model significantly improved by utilizing the results of the clustered predictors for 40 batteries in a real data center. One year of actual historical data of 40 batteries of large scale datacenter is presented to validate the effectiveness of the proposed methodology.
Subject Areas
forecasting; clustering; energy systems; classification
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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