Ensemble Machine Learning Framework for Real-Time Energy Demand Forecasting and Dynamic Load Optimization in Multi-MW Hyperscale Computing Infrastructure: An ERCOT Market Case Study
Author(s): Sai Kothapalli
Publication #: 2506012
Date of Publication: 09.01.2024
Country: United States
Pages: 1-12
Published In: Volume 10 Issue 1 January-2024
DOI: https://doi.org/10.5281/zenodo.15614773
Abstract
This paper presents a comprehensive machine learning approach for predicting and optimizing electricity consumption in hyperscale data centers, focusing on a 60-megawatt facility in Austin, Texas. With data centers consuming approximately 1% of global electricity, accurate consumption prediction is critical for operational efficiency and cost management. This research implements multiple ML algorithms including Random Forest, LSTM neural networks, and XGBoost to forecast hourly electricity consumption based on server utilization, ambient temperature, cooling loads, and temporal patterns. The results demonstrate that ensemble methods achieve a Mean Absolute Percentage Error (MAPE) of 3.2% for 24-hour forecasts and 5.8% for 7-day forecasts. The predictive models enable proactive load management, reducing peak consumption by 12% and operational costs by $2.3M annually. The Austin case study reveals unique challenges including extreme summer temperatures reaching 40°C and volatile renewable energy pricing from ERCOT markets.
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