Energy-efficient strategic management of data center with artificial intelligence application

Modern data centers (DCs) face the problem of high energy consumption, which leadsto increased operating costs and negative environmental impact. This raises the need to developintelligent control systems (ICS) that can improve energy efficiency and optimize the use ofengineering infrastructure.The aim of this work is to evaluate the effectiveness of integrating neural networks and a robustPID controller into an IMS to improve control adaptability, reduce energy consumption andincrease the resilience of data centers to changing operating conditions.Methods: in order to achieve the set objectives, a simulation model of the TIER IV level datacenter was developed in the TRNSYS environment, which allows to simulate dynamicprocesses of energy consumption. The model uses machine learning algorithms to predictthermal load and power consumption, implemented using a neural network trained in MATLABenvironment. A robust PID controller is also implemented to control cooling systems based onthe predicted data. An economic analysis of the efficiency of IMS implementation with thecalculation of key indicators: ROI, NPV and BCR.The novelty of the work consists in proposing an approach to balancing the input data forneural networks, which allows to reduce the spread of amplitudes of oscillations, reduce theprobability of overtraining and increase the generalization ability of the network. For the firsttime, a method of integrating neural networks and robust PID controller for data center energymanagement, taking into account dynamic infrastructure changes, has been developed.Result: The proposed control system reduced the PUE by 3.5%, reduced the powerconsumption of the cooling systems to 40% of the total power consumption of the data centerand improved the accuracy of heat load forecasting. This increased the adaptability of thecontrol and provided a reduction in operating costs.Practical significance: the developed methods and models are applicable for modernization ofdata center engineering infrastructure in order to improve their energy efficiency, reduce costsand ensure environmental sustainability. The results show the feasibility of implementing IMSto improve the resilience and adaptability of data centers to changing operating conditions,which helps to reduce operating costs and environmental impact.