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.
Highlights
Our proposed work shows that the forecasting accuracy of the Auto-Regressive Integrated Moving Average (ARIMA) model is significantly improved by applying the results of the clustered predictor for batteries in a real data center
Uninterrupted power source (UPS) batteries are an integral part of any data center, which ensure the stable performance of the data center during transitional fail-over mechanisms between power grids and diesel generators [1]
Our objective is to develop a scalable clustering framework to improve the forecasting accuracy of the ARIMA model for battery voltages in data centers
Summary
Uninterrupted power source (UPS) batteries are an integral part of any data center, which ensure the stable performance of the data center during transitional fail-over mechanisms between power grids and diesel generators [1]. Data centers require steady power for smooth performance, which is managed by the UPS batteries. UPS is installed between the main power grid and the servers [2]. Since the electricity bill of a data center constitutes a significant portion of its overall operational costs, data centers are major consumers of electrical energy [3]. In 2013, data centers in U.S.A. consumed. In 2017, nearly 8 million data centers required an astronomical 416.2 terawatt-hours of electricity [5,6]
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