中圖分類號(hào)：TP391 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.246045 中文引用格式： 宋越，靳晟，林櫟，等. 基于CNN-BiLSTM-Attention的工業(yè)數(shù)據(jù)中心IT設(shè)備能耗預(yù)測(cè)模型研究[J]. 電子技術(shù)應(yīng)用，2025，51(10)：63-68. 英文引用格式： Song Yue，Jin Sheng，Lin Li，et al. Research on energy consumption prediction model of industrial data center IT equipment based on CNN-BiLSTM-Attention[J]. Application of Electronic Technique，2025，51(10)：63-68.
Research on energy consumption prediction model of industrial data center IT equipment based on CNN-BiLSTM-Attention
Song Yue1，Jin Sheng1，Lin Li2，Gao Guoqiang2，Guo Fuzhan2
1.College of Computer and Information Engineering，Xinjiang Agricultural University；2.Xinjiang Institute of Electronics Co.，Ltd.
Abstract： The energy consumption of IT equipment directly affects the power consumption of industrial data centers, and predicting the energy consumption of IT equipment is of great significance for optimizing energy management and resource planning. However, due to the non-linear and non-stationary nature of IT energy consumption data, the prediction accuracy is low. In this regard, by combining the advantages of convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM), and attention mechanism, local features of IT equipment energy consumption and deep key information in the data are extracted separately. Based on the self tested IT equipment energy consumption dataset, an energy consumption prediction model based on CNN-BiLSTM-Attention is constructed. The R2, MAE, and RMSE of this model are 0.905 3, 0.050 4, and 0.067 3, respectively. Compared with existing LSTM, BiLSTM and CNN-BiLSTM models, this model has improved to varying degrees, indicating that this model can be applied to accurate prediction of IT equipment energy consumption in industrial data centers.
Key words : IT energy consumption prediction model；CNN-BiLSTM-Attention；industrial data center；deep learning
