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

Abstract

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%.

Keywords

• data center
• model predictive control
• control strategy
• energy conservation
• chiller