Short-term Power Load Forecasting Method of Data Center Considering PUE - WU Jin-song; ZHANG Shao feng; XU Xiang-min; LI Shu-tao; HUANG Yong; LIAO Xiao (Journal of Harbin University of Science and Technology)
In order to accurately predict the short-term power load of data centers, a short-term load forecasting model based on long- and short-term memory neural networks is proposed, which effectively compensates for the shortcomings of feed forward neural networks that cannot process the correlation information between sequences and traditional recurrent neural networks cannot remember long-term key information.Through analysis, it is concluded that the power usage effectiveness (PUE) value is correlated with the load. Therefore, the influence of PUE is considered in the prediction model, and the adaptive moment estimation algorithm is used for deep learning. Finally, by predicting the actual power load of the data center computer room of a certain electric power design institute in Guangzhou, introducing the PUE value into the model can effectively improve the accuracy of the short-term load forecast of the data center.