Multi-Agentic AI End-to-End Project| Building a Smart Home Energy Optimizer

Описание: Welcome to one of the most complete end-to-end AI engineering projects — The Home Energy Saver AI Agent System!

📦 Resources & Links:
Code: https://github.com/Sandesh-hase/Smart...

Introduction to Microsoft Agent Framework:    • Introduction to Microsoft Agent Framework  

LangGraph Tutorial:    • LangGraph End-to-End Series | From Basics ...  

Agentic RAG-Part-1:    • RAG vs Agentic RAG: From Basics to Researc...  
Agentic RAG-Part-2:    • Building a Single-Agent Agentic RAG | Mult...  

Agent Foundry Mastery:    • AI Agent in Just 5 Minutes with Azure 🤯  

Autogen Playlist:    • Agentic AI with AutoGen – From Zero to Pro...  

Azure AI Foundry:Prompt Flow, Finetuning GPT4-o, RAG, LLMOps
: https://www.udemy.com/course/develop-...

Agentic AI Developemnt with Azure & Semantic Kernel Course: https://www.udemy.com/course/agentic-...

MCP:    • Build Your First MCP – Model Context Proto...  

In this nearly 2-hour in-depth tutorial, I walk you through building a real-world, production-ready Generative AI project from scratch — powered by the Microsoft Agent Framework (MAF), Azure OpenAI, and a beautiful Streamlit UI frontend.

#GenerativeAI #MicrosoftAgentFramework #StreamlitApp #AzureOpenAI #AIAgent #energyoptimization

This project demonstrates how you can combine Machine Learning, Multi-Agent AI systems, and real-time data orchestration to optimize home energy usage intelligently. The system doesn’t just forecast energy — it thinks, analyzes, and recommends actionable insights to reduce cost and power consumption for households.
🔧 Key Components of the Project

Microsoft Agent Framework (MAF) — The core of our agentic intelligence.

Built two specialized agents:

🧩 Energy Usage Analysis Agent → Reads the latest appliance data, analyzes past usage, and fetches tomorrow’s weather forecast using Open-Meteo API.

⚙️ Energy Optimizer Agent → Compares past vs. predicted energy patterns and generates optimized appliance-level recommendations using Azure OpenAI.

Machine Learning Integration

Uses a Prophet-based forecasting model to predict next-day energy consumption for each appliance (Air Conditioner, Dishwasher, Microwave, Washing Machine, Computer, etc.).

Combines this forecast with environmental data to drive energy-saving logic.

Dynamic CSV Integration

Automatically reads the latest data from appliance_usage.csv and correlates it with weather information and model predictions.

Energy Recommendation Engine

Generates smart suggestions such as:

“Set AC to 25°C between 6PM–10PM → Save ₹14.5”

“Run dishwasher in off-peak hours → Save ₹2.2”

Calculates both estimated kWh savings and cost savings dynamically.

Azure OpenAI Integration

Utilizes gpt-4o-mini via the Microsoft Agent Framework for decision reasoning, summarization, and email generation.

Automated Email Reports

The system automatically formats and sends a daily optimization report using LLM-based natural language summarization and SMTP integration, including friendly, emoji-enhanced insights.

Streamlit Frontend UI

A modern and responsive dashboard built with Streamlit, showing:

Input parameters (household size, location, appliances)

Energy-saving gauge meters 💡

Total estimated kWh and cost savings in real-time

Beautifully styled recommendation cards

Email trigger button for daily energy reports

⚙️ Tech Stack

🧠 Microsoft Agent Framework (MAF)

☁️ Azure OpenAI (gpt-4o-mini)

📈 Prophet ML Model for Forecasting

🐍 FastAPI Backend

📧 SMTP + LLM-based Email Agent

🎨 Streamlit for UI

🧾 Pandas, Pydantic, Requests

💬 What You’ll Learn

How to design and implement multi-agent AI workflows

Integrate machine learning predictions with LLM-driven reasoning

Create autonomous, decision-making AI systems

Build clean, user-friendly frontend dashboards with Streamlit

Generate and send AI-formatted reports automatically

🌟 Why This Project Is Special

This project represents the next generation of Agentic AI systems — where AI Agents don’t just predict outcomes, but also reason, optimize, and act autonomously. It’s a fully functional prototype that bridges machine learning, generative AI, and automation — making it perfect for enterprise-grade use cases like energy management, sustainability dashboards, or smart homes.

🔔 Subscribe & Stay Ahead!

If you found this valuable, make sure to subscribe, like, and comment below — because the next video will take this even further into autonomous decision-making using LangGraph and multi-agent collaboration.