作者:吴劲松,张少峰,徐向民,李舒涛,黄湧,廖霄
Authors:WU Jin-song,ZHANG Shao feng,XU Xiang-min,LI Shu-tao,HUANG Yong,LIAO Xiao摘要:摘要:为了准确预测数据中心短期电力负荷，提出了基于长短期记忆神经网络的短期负荷预测模型，有效地弥补前馈型神经网络不能处理序列间关联信息与传统循环神经网络无法记忆久远关键信息的缺陷。通过分析得出电源利用效率（power usage effectiveness , PUE）值与负荷具有相关性，因此在预测模型中考虑了PUE的影响，并使用自适应矩估计算法进行深度学习。并通过对广州某电力设计院数据中心机房的实际电力负荷进行预测，表明在模型中引入PUE值可以有效提高数据中心短期负荷预测的精度。
Abstract:Abstract: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.

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