Data Center Efficiency

We are actively developing an AI-driven model to predict data center efficiency using data from geographically diverse locations. Our goal is to provide data center administrators, contractors, and tech companies with accurate forecasts to optimize performance. By analyzing Traveling Twelve-Month Power Usage Efficiency (TTM PUE) alongside environmental factors, we aim to uncover complex interactions that impact efficiency in specific locations.

Our predictive model is designed for data centers in moderate climates, with the ability to forecast trends in extreme conditions. By analyzing data from global data centers, we account for factors such as climate change, technological advancements, cooling strategies, and overall costs to predict future efficiency.

Here is an interactive plot illustrating our model's performance. We continuously refine its accuracy, improve interoperability, and explore innovative data collection strategies to enhance predictive capabilities.

Our model allows users to input independent environmental variables to forecast TTM PUE. We are actively developing additional features to enhance predictive accuracy. Beyond analyzing the impact of climate variables on data center efficiency, we aim to explore how energy consumption affects economic risk and operational costs. As the project evolves, we plan to integrate financial and energy consumption data for a more comprehensive analysis.

Reach out to us for upcoming updates!