Optimizing Energy Efficiency in Data Centers Through Machine Learning Model

The power consumption of data centers has risen sharply due to the increasing need for data processing and storage. Addressing this issue requires optimizing the energy efficiency of these assets through effective design and operation. This study aims to solve this problem by creating a machine learning-based method to forecast the energy consumption of future data centers. We collected a dataset of data center energy consumption and performance data. We applied machine learning models to this dataset to predict energy consumption under various operating conditions. Our results demonstrate that machine learning techniques can adequately project data centers and identify key factors affecting energy efficiency. These insights can inform the design and operation of future energy-efficient data centers, lowering environmental impact and energy use.