Prediction of battery status in cloud data centers based on linear SVM algorithms

Abstract

Valve regulated lead-acid (VRLA) battery aging has threatened power supply reliability in cloud data centers, aimed at this problem, a method of an online battery state prediction method based on machine learning algorithms was proposed. The classic machine learning algorithm model was used to predict the battery status, and the prediction accuracy unextracted and the extracted feature values model was compared. The prediction accuracy of the model without extracted feature values ranges from 71.72% to 81.82%, and after extracting feature values, the prediction accuracy has improved by 10.10% to 20.20%, extracting feature values can improve prediction accuracy. The prediction model based on linear SVM method for extracting feature values is superior to other algorithms, with an accuracy of 96.46%. Experiment results show that machine learning algorithm based prediction models can be used for online VRLA battery state prediction in cloud data centers.

Keywords

• cloud data center
• valve regulated lead-acid battery
• gaussian filter
• linear discrimination
• machine learning algorithm