数据驱动下的数据中心温度预测研究综述OA

With the growing concern over energy consumption and heat dissipation in data centers,temperature control has become a pivotal factor in maintaining operational efficiency and extending equipment lifespan.Accurate temperature prediction plays a vital role in this process.However,traditional methods primarily rely on physical models and rule-based frameworks,which struggle to adapt to complicated and changeable operational environment.In recent years,data-driven methodologies have emerged as a promising alternative.This review systematically reviews data-driven research on data center temperature prediction,focusing on core dimensions such as hierarchical structure,time scales,and cooling systems.It refines core concept definitions,analyzes data types and feature distributions,and evaluates their influence on model performance.It synthesizes research progress in machine learning,deep learning,and hybrid modeling approaches,highlighting their contributions to enhancing predictive accuracy and robustness.The review indicates that deep learning models are particularly effective in capturing complex spatiotemporal dependencies,while hybrid models offer comple-mentary advantages by integrating the strengths of multiple paradigms.Potential research directions are discussed,includ-ing multimodal data fusion,adaptive learning,and edge-intelligent temperature prediction frameworks.