Designing Climate-Adaptive Data Centers Using Predictive Cooling Models

Abstract Data centers are facing mounting pressure from climate change and escalating compute demands, which together intensify cooling requirements and energy use. This paper investigates the design of climate-adaptive data centers that leverage predictive cooling models powered by AI/ML to optimize thermal management. We review emerging cooling technologies and control strategies, including airflow economizers, liquid cooling, and hybrid systems, within the context of climate variability[۱][۲]. We then survey predictive modeling methodologies – such as neural network forecasting, model-predictive control (MPC), and reinforcement learning (RL) – that dynamically adjust cooling setpoints based on forecasts of workload and ambient conditions[۳][۴]. Simulated and real-world case studies demonstrate that these approaches can achieve significant energy and water savings: for example, transformer-based models achieved higher accuracy in predicting coolant temperature and “effectively reducing the operational energy consumption” of liquid cooling systems[۳][۵]. We discuss implementation challenges (data requirements, model robustness, safety constraints) and outline future research directions, including digital twins and grid-interactive controls. In conclusion, climate-adaptive predictive cooling can substantially improve data center efficiency and resilience, offering a roadmap toward sustainable, intelligent infrastructure.

کلیدواژه ها:

Designing Climate ، Adaptive Data Centers ، Rreinforcement Learning (RL) ، model-predictive control (MPC) ، Liquid Cooling Systems