High-Performance Computing for Predicting the Environmental Impact of Renewable Energy Sources

In the context of great global attention to energy security, the development and utilization of renewable energy have become a key measure to achieve the goal of high-quality development and "two-carbon." Our work is to study the ecological impact of HPC analysis. Three different machine learning models—Support Vector Regression (SVR), Random Forest, and Gradient Boosting—are employed in this study to predict utilization rates based on factors like energy mix and workload characteristics. An ARIMA (Autoregressive Integrated Moving Average) model forecasts future energy consumption trends based on historical data from different data center types (Traditional, Cloud, and Hyperscale). These analyses show a worrying increase trend in energy use and carbon emissions. In addition, a SARIMA (Seasonal ARIMA) model is fitted to forecast future energy consumption over various economic sectors (residential, commercial, and industrial), including historical trends and seasonality. That enables a more complete analysis of the entire energy landscape and how it is weighed toward HPC. The model analyzes several future energy mix scenarios, from a gradually maintained role of fossil fuels to rapidly increasing adoption of renewables. The study estimates the possible contributions of increasing renewable penetration to reducing HPC's carbon footprint.