Research on data center load energy consumption forecasting models based on deep learning
LIU Qian
ZHOU Quan
YE Xiao-jiang
Abstract：To enhance the accuracy of energy consumption forecasting for data centers,a deep learning-based model for predicting data center load energy consumption is proposed.This model is based on historical load data and integrates environmental parameters of the data center,utilizing long short-term memory(LSTM)neural networks for forecasting.A real energy consumption dataset from a certain data center was selected and randomly divided into training,validation,and testing sets in a ratio of 8:1:1,which allows for the verification of the model's forecasting effectiveness.The results show that compared to traditional time series forecasting methods,the model's prediction accuracy and stability have significantly improved.The model's mean absolute error(MAE)and root mean square error(RMSE)were reduced by 36.2%and 34.2%,respectively,compared to the autoregressive integrated moving average(ARIMA)model,and the mean absolute percentage error(MAPE)was also reduced by 24.4%.
Keywords：data centerload power consumption predictiondeep learningLSTM neural networkenvironmental parameters
Publication Date：2024-11-15
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：3( 98-100 )