balaprasannav2009/data-center-energy-optimization

⚡ AI-Powered Data Center Energy Optimization Dashboard

Overview

This dashboard uses machine learning to predict and optimize energy consumption in data centers. It provides real-time insights and actionable recommendations to reduce operational costs and carbon footprint.

Features

• —📊 Real-time energy consumption predictions
• —💡 AI-powered optimization recommendations
• —📈 Interactive visualizations and analytics
• —🎛️ Adjustable parameters for scenario testing
• —📋 Historical data analysis

Machine Learning Models

The system uses three regression models:

• —Linear Regression
• —Random Forest (Best performing)
• —XGBoost

How to Use

1. 1.Adjust the parameters in the sidebar (Server Load, Temperature, Cooling Efficiency, etc.)
2. 2.Click "Predict & Recommend" to get energy predictions
3. 3.Review the AI-generated recommendations
4. 4.Explore the visualizations in different tabs

Project Context

This is a capstone project demonstrating AI application for:

• —Predictive analytics in infrastructure management
• —Energy efficiency optimization
• —Sustainability and cost reduction
• —Real-time decision support systems

Technologies Used

• —Frontend: Streamlit
• —ML Models: Scikit-learn, XGBoost
• —Data Processing: Pandas, NumPy
• —Visualization: Plotly
• —Deployment: Docker

Author

Created as part of CAIO (Chief AI Officer) certification capstone project.