Cluster-Based Prediction for Batteries in Data Centers
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 fir...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-03-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/13/5/1085 |
| _version_ | 1798043654501171200 |
|---|---|
| author | Syed Naeem Haider Qianchuan Zhao Xueliang Li |
| author_facet | Syed Naeem Haider Qianchuan Zhao Xueliang Li |
| author_sort | Syed Naeem Haider |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T22:52:05Z |
| format | Article |
| id | doaj.art-bb5ea7063c2d42ff9c01bc0612effd0b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T22:52:05Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-bb5ea7063c2d42ff9c01bc0612effd0b2022-12-22T03:58:34ZengMDPI AGEnergies1996-10732020-03-01135108510.3390/en13051085en13051085Cluster-Based Prediction for Batteries in Data CentersSyed Naeem Haider0Qianchuan Zhao1Xueliang Li2Center for Intelligent and Networked Systems (CFINS), Department of Automation and BNRist, Tsinghua University, Beijing 100084, ChinaCenter for Intelligent and Networked Systems (CFINS), Department of Automation and BNRist, Tsinghua University, Beijing 100084, ChinaCenter for Intelligent and Networked Systems (CFINS), Department of Automation and BNRist, Tsinghua University, Beijing 100084, ChinaPrediction 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.https://www.mdpi.com/1996-1073/13/5/1085forecastingclusteringenergy systemsclassification |
| spellingShingle | Syed Naeem Haider Qianchuan Zhao Xueliang Li Cluster-Based Prediction for Batteries in Data Centers Energies forecasting clustering energy systems classification |
| title | Cluster-Based Prediction for Batteries in Data Centers |
| title_full | Cluster-Based Prediction for Batteries in Data Centers |
| title_fullStr | Cluster-Based Prediction for Batteries in Data Centers |
| title_full_unstemmed | Cluster-Based Prediction for Batteries in Data Centers |
| title_short | Cluster-Based Prediction for Batteries in Data Centers |
| title_sort | cluster based prediction for batteries in data centers |
| topic | forecasting clustering energy systems classification |
| url | https://www.mdpi.com/1996-1073/13/5/1085 |
| work_keys_str_mv | AT syednaeemhaider clusterbasedpredictionforbatteriesindatacenters AT qianchuanzhao clusterbasedpredictionforbatteriesindatacenters AT xueliangli clusterbasedpredictionforbatteriesindatacenters |