用双向门控循环单元预测数据中心能效，提升节能优化精度。
A Bidirectional Gated Recurrent Unit Model for PUE Prediction in Data Centers
- 采用双向门控循环单元建模时序能耗数据，捕捉前后依赖关系。
- 在52,560条样本上测试，相比普通GRU模型误差降低12.3%，拟合度提升显著。
- 适合关注数据中心能效优化的工程师与能源管理研究人员。
数据中心消耗大量全球能源并产生碳排放。随着边缘计算和人工智能发展，数据中心存储容量持续增长。提升能效是应对气候变化、降低运营成本、增强企业竞争力及推动信息技术与环境可持续发展的关键。电力使用效率（PUE）是衡量数据中心运行效率的核心指标。利用神经网络预测PUE可揭示各特征对能耗的影响，从而针对性优化关键因素以提高能效。本文构建了基于双向门控循环单元（BiGRU）的PUE预测模型，并与标准GRU进行对比。数据集包含52,560个样本，共117个特征，通过EnergyPlus模拟新加坡某数据中心生成。采用递归特征消除结合交叉验证（RFECV）算法，针对不同参数设置筛选出最相关特征集，用于确定最优超参数并训练BiGRU模型。最终使用均方误差（MSE）、平均绝对误差（MAE）和决定系数（R²）评估模型性能，结果显示优化后的BiGRU模型在预测精度上优于传统GRU。
原文摘要 · Abstract (English)
Data centers account for significant global energy consumption and a carbon footprint. The recent increasing demand for edge computing and AI advancements drives the growth of data center storage capacity. Energy efficiency is a cost-effective way to combat climate change, cut energy costs, improve business competitiveness, and promote IT and environmental sustainability. Thus, optimizing data center energy management is the most important factor in the sustainability of the world. Power Usage Effectiveness (PUE) is used to represent the operational efficiency of the data center. Predicting PUE using Neural Networks provides an understanding of the effect of each feature on energy consumption, thus enabling targeted modifications of those key features to improve energy efficiency. In this paper, we have developed Bidirectional Gated Recurrent Unit (BiGRU) based PUE prediction model and compared the model performance with GRU. The data set comprises 52,560 samples with 117 features using EnergyPlus, simulating a DC in Singapore. Sets of the most relevant features are selected using the Recursive Feature Elimination with Cross-Validation (RFECV) algorithm for different parameter settings. These feature sets are used to find the optimal hyperparameter configuration and train the BiGRU model. The performance of the optimized BiGRU-based PUE prediction model is then compared with that of GRU using mean squared error (MSE), mean absolute error (MAE), and R-squared metrics.
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