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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.

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Abstract. Deep integration of industrial system and information technology triggered the fourth industrial revolution-Industry 4.0 which based on cyber physical system and Industrial Big Data. Although many researchers have discussed the basic concept of industry 4.0 and Industrial Big Data, as we known no literatures about how to design and develop an Industrial Big Data platform. Business solutions are generally not open to the public, so little is known about how to achieve it. Open source big data tools have widely used in the Internet field, but it is not clear how these tools are used in the industrial field. We focus on how to use open source big data tools to build a big data platform for industrial systems and a systematically designed framework is proposed including data acquisition, transmission, processing, storage and visualization.

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