International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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GreenAI: Cloud-Based AI System for Carbon Emission Forecasting and GreenOps Optimization

Author(s) Ananya K, Anusha L, Bindu A, Preethi K R, Dr. Hanumanthappa S
Country India
Abstract Cloud computing infrastructure is a growing contributor to global carbon emissions, yet most organizations have limited visibility into the environmental impact of their workloads. This work presents GreenAI, a full-stack cloud-based artificial intelligence (AI) sustainability platform that monitors cloud resource utilization, converts energy consumption into carbon emissions using region-specific grid carbon-intensity factors, forecasts future emissions using Random Forest and Extreme Gradient Boosting (XGBoost) models, and generates explainable rule-based GreenOps optimization recommendations. A background scheduler runs a daily monitoring cycle that forecasts next-day emissions, scores each earlier forecast against what actually occurred, detects statistical anomalies in server behavior, checks a configurable carbon budget, and dispatches Slack and e-mail notifications. A real-time streaming mode and an optional Amazon CloudWatch integration show that the architecture generalizes beyond simulated data. The backend is implemented in Python using FastAPI with MongoDB as the data store, and the frontend is a React single-page application with nine views. On a synthetic but realistically correlated dataset of 4,800 records (12 servers, 400 days), Random Forest and XGBoost achieved coefficient of determination (R2) values of 0.9744 and 0.9738 with mean absolute errors of 2.813 and 2.852 kg of carbon dioxide (CO2) on held-out data. The complete system passed an automated suite of 47 unit, integration, and application programming interface (API)-level tests. The demonstrated platform provides a foundation for cloud carbon accounting, emission forecasting, and sustainable cloud operations (GreenOps).
Keywords Anomaly Detection, Carbon Footprint, Cloud Computing, GreenOps, Machine Learning, XGBoost
Field Engineering
Published In Volume 8, Issue 5, September-October 2026
Published On 2026-10-07
DOI https://doi.org/10.36948/ijfmr.2026.v08i05.89223
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