Graduate Project

Improving Peak Power Shaving in Data Centers using Deep Neural Networks

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  • Data centers have been charged a great amount of electric bill by the power company and demand charge can contribute up to 40% of the electric bill due to the "random" workload. This phenomenon can be avoided by using the existing Uninterrupted Power Supply (UPS) as the assistant power source to supply the servers. The UPS will shave the peak power by supporting the servers while the peak power exceeds threshold during the on-peak duration. For using the UPS to compensate the extra power, the problems are predicting the future data center resource usage and predicting the remaining capacity in the UPS battery. Since both problems involve in a highly correlated temporal relationship among their data, adopting Long-Short-Term-Memory(LSTM) Neural Network to perform the prediction solves the problems. After the experiment, the Long-Short-Term-Memory(LSTM) Neural Network solves the Resources Usage Prediction problem by having 6% Root Mean Square Error(RMSE) which outperforms the others and UPS Remaining Capacity achieves 7% RMSE.
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