基于模型预测控制的数据中心水蓄冷冷却系统节能优化模型

Energy-saving optimization model for on data center water storage cooling system based on model predictive control

为了降低数据中心冷却系统能耗,保障数据中心的健康快速发展,以广州某数据中心冷却系统为研究对象,针对冷水机组在不同负载率下能效存在差异的特性,提出了一种新型模型预测控制方法。通过高斯过程回归建立冷水机组运行能效模型,长短期记忆神经网络构建室外湿球温度时序预测模型,并根据数据中心冷负荷和室外湿球温度预测结果,调整冷却系统的蓄放冷模式、蓄放冷量、冷水机组开启数量以及部分负载率。结果表明,实现了需求侧和供应侧的解耦,使冷水机组要么运行在高效区间,要么关闭,降低了冷却系统的运行能耗。与传统基于规则控制的策略相比,模型预测控制策略将总能耗减少了3.13%,运行费用减少了3.33%。

To reduce energy consumption in data center cooling systems and ensure the healthy and rapid development of data centers,this study investigated on the cooling system of a data center in Guangzhou.Aimed at the efficiency variations of chillers at different Partial Load Rates(PLR),a novel model predictive control method was proposed.The study employed Gaussian process regression to establish a chillers′operational efficiency model and utilized Long Short-Term Memory(LSTM)neural networks to create an outdoor wet bulb temperature time series prediction model.The cooling system′s chilled water storage and discharge modes,storage and discharge capacity,the number of chillers,and the PLR were adjusted based on the data center cooling load and the prediction of outdoor wet bulb temperature.This adjustment aims to partially decouple the demand and supply sides,resulting in the chillers being operated in high efficiency or turned off,thereby reducing the operational energy consumption of the cooling system.Compared to the traditional rule-based control strategy,the model predictive control strategy reduced the overall energy consumption by 3.13%and operating costs by 3.33%.