Optimization of Energy Efficiency of Data Center Cooling Systems

As of 2018, China had more than 550,000 data centers, accounting for approximately 1.5% of the country's total annual electricity consumption. Reducing energy consumption not only lowers costs but also aligns with environmental protection principles. Current research primarily focuses on IT equipment and air conditioning and refrigeration systems, as these components offer significant potential for energy savings. Instead of making adjustments to the original space environment and hardware facilities of the data center, this study proposes an energy efficiency optimization model by regulating the set values and parameters of the cooling system. This approach is particularly valuable given the increasing emphasis on energy efficiency in data centers. The cooling system of a data center is highly complex, with multiple variables, high feature dimension, time-varying working conditions, and strong uncertainty. This study employs an artificial neural network to model the energy efficiency (PUE) of a data center, using data collected from the operation of the cooling system in a data center in southwest China. Subsequently, an evolutionary algorithm is used to analyze and optimize the cooling system, controlling the set values to enhance the energy efficiency of the data center.