Optimizing Data Center Energy Consumption Using Machine Learning and Monte Carlo Simulation

This study proposes a joint optimization framework that integrates machine learning and Monte Carlo simulation, aiming to systematically reduce the energy consumption and carbon emissions of data centers. By constructing a multi-source energy consumption model to quantify the dynamic coupling relationship between servers, cooling systems, and energy supply systems, a low-carbon energy structure is generated by combining LSTM prediction and Monte Carlo random sampling, significantly optimizing the carbon emission level. Experiments show that the dynamic adjustment strategy driven by reinforcement learning can effectively improve system stability, and extreme scenario analysis further reveals the significant impact of the synergistic effect of load peak and high temperature environment on carbon emissions. The model analyzes the nonlinear mechanism of the role of renewable energy proportion and load rate on carbon emissions through sensitivity analysis, providing methodological support for the low-carbon transformation of high-energy consumption infrastructure.