Improving Energy Efficiency in a Data Center: PUE Analyzing and Tuning

In the digital era, energy efficiency in data centers is crucial due to the exponential growth of data and the increasing demand for technological infrastructures. Power Usage Effectiveness (PUE) is a key indicator for evaluating this energy efficiency, measuring the ratio between the total energy consumption of a data center and the energy used by information technology (IT) equipment. Integrating sensors to monitor key variables provides a detailed and comprehensive view of data center operations. However, analyzing these large volumes of complex data requires advanced processing approaches. In this context, machine learning technologies play a decisive role, as learning algorithms can identify hidden patterns and correlations that would be difficult to detect with traditional methods. This research presents a step-by-step methodology to optimize data center operations by combining advanced sensor technologies and machine learning. It identifies key variables, integrates sensors to monitor them, and analyzes the data to reveal hidden patterns that traditional methods may miss. This approach enables realtime, data-driven decisions, improving efficiency, reducing energy consumption, and optimizing PUE. Validated through a real use case, the methodology demonstrates its potential to enhance energy management and promote sustainability in data centers.