Water Management in Data Centers Using Ensemble Learning Models

Data centers are using enormous volumes of water for maintenance and cooling as a result of the growing need for computational power. Reducing operating expenses and the environmental impact requires effective water management. In order to minimize water usage in data centers, this study proposes a predictive analytics framework that makes use of ensemble learning techniques, specifically Random Forest Regressor, XGBoost Regressor, and Gradient Boosting Regressor. To find patterns of excessive consumption, the suggested method combines anomaly detection with real-time monitoring. To improve the effectiveness of predictive models, the framework uses a multifaceted strategy that includes adaptive thresholding, outlier detection, and time-series forecasting. Significant gains in anomaly detection rates and forecast accuracy are shown by a comparative performance analysis. Additionally, proactive intervention is made possible by the integration of real-time information, which lowers water waste and guarantees operational sustainability. The experimental findings demonstrate the suggested methodology’s potential for widespread implementation in data centers and prove its resilience.