⚡ GridMind AI – Autonomous Decision Engine for Smart Energy Grids

🚀 An AI-powered platform that predicts energy demand, optimizes grid decisions, and explains AI reasoning in real time for modern energy networks.

✨ Features

GridMind AI combines predictive AI, reinforcement learning, and explainable AI to help energy operators make optimal decisions in real time.

• ⚡ Demand Forecasting – Predicts next-hour energy demand using Random Forest ML model
• 🧠 AI Decision Engine – Recommends optimal grid actions (battery discharge/charge, load reduction, grid import)
• 📊 Explainable AI – Feature importance visualization showing what influences predictions (Temperature, Hour, Solar, Load)
• 🎮 Scenario Simulator – Interactive controls to simulate different conditions (temperature, solar output, load, time)
• 👥 Multi-Role Access – Role-based dashboards for Executive, Operator, Consumer, and Regulator

🎯 Use Cases & Roles

Role	Description
⚡ Energy Executive	Executive-level analytics, strategic insights, and high-level grid performance metrics
🎛️ Grid Operator	Real-time grid monitoring, AI decisions, and operational control center
🏠 Consumer	Personal energy usage tracking, consumption analytics, and billing management
📋 Regulator	Compliance monitoring, grid reliability metrics, and regulatory reporting

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⚙️ Platform Support

Platform	Minimum Requirements	Supported?
Web Application (Fully Responsive)	Modern Browser (Chrome, Brave, Edge, Firefox, etc)	✅

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🛠️ Tech Stack

Frontend

• Next.js 16
• React 19
• Tailwind CSS v4
• TypeScript
• Recharts (Data Visualization)
• shadcn/ui Components
• Framer Motion (Animations)

Backend

• Framework: FastAPI (Python)
• ML: scikit-learn (Random Forest Regressor)
• Data Processing: pandas, numpy

ML Models

• Random Forest – Demand forecasting with 100 trees
• Feature Scaling – StandardScaler for normalization
• Feature Set: hour, day_of_week, temperature, solar_output, current_load

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🚀 Getting Started

1️⃣ Clone the Repository

cd smart-energy-grids

2️⃣ Backend Setup

cd backend/app

# Install dependencies
pip install -r requirements.txt

# Train the ML model
python train_model.py

# Run the server
python -m uvicorn main:app --host 0.0.0.0 --port 8000

The API will be available at http://localhost:8000

• API Docs: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

3️⃣ Frontend Setup

cd frontend
npm install
npm run dev

Open http://localhost:3000

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📁 Folder Structure

smart-energy-grids/
│
├── backend/                 # FastAPI backend
│   ├── app/                # Application modules
│   │   ├── __init__.py
│   │   ├── main.py         # FastAPI server entry point
│   │   ├── predict.py      # ML prediction model
│   │   ├── decision_engine.py  # AI decision logic
│   │   └── train_model.py  # Model training script
│   ├── data/               # Training data
│   │   └── energy_data.csv
│   ├── models/             # Trained ML models
│   │   ├── energy_model.pkl
│   │   ├── scaler.pkl
│   │   └── features.pkl
│   └── requirements.txt    # Python dependencies
│
├── frontend/               # Next.js frontend
│   ├── src/
│   │   ├── app/           # App router pages
│   │   │   ├── (public)/ # Landing page
│   │   │   ├── auth/      # Role selection
│   │   │   ├── app/       # Original dashboard
│   │   │   ├── executive/ # Energy Executive dashboard
│   │   │   ├── operator/  # Grid Operator dashboard
│   │   │   ├── consumer/  # Consumer dashboard
│   │   │   └── regulator/ # Regulator dashboard
│   │   ├── components/   # UI components
│   │   │   └── ui/        # shadcn components
│   │   └── lib/           # Utilities & API client
│   │       ├── api.ts
│   │       └── utils.ts
│   └── package.json
│
├── CONTEXT.txt            # Project requirements & design
└── README.md

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🏛️ Project Architecture

graph TB
    subgraph Client["Frontend (Web)"]
        Landing[Landing Page]
        Auth[Role Selection]
        Exec[Executive Dashboard]
        Oper[Operator Dashboard]
        Cons[Consumer Dashboard]
        Regul[Regulator Dashboard]
    end

    subgraph Backend["Backend (FastAPI)"]
        API[API Routes]
        Pred[Prediction Model]
        Decision[Decision Engine]
    end

    subgraph ML["ML Pipeline"]
        RF[Random Forest]
        Scaler[StandardScaler]
    end

    subgraph Data["Data Layer"]
        CSV[(Energy Data)]
        Models[(Trained Models)]
    end

    subgraph IoT["Demo Data Streaming ⚠️"]
        Sim[Simulation Script]
    end

    Landing --> Auth
    Auth --> Exec
    Auth --> Oper
    Auth --> Cons
    Auth --> Regul

    Exec -->|HTTP| API
    Oper -->|HTTP| API
    Cons -->|HTTP| API
    Regul -->|HTTP| API

    API --> Pred
    API --> Decision
    Pred --> RF
    Pred --> Scaler
    
    RF --> Models
    Scaler --> Models
    CSV -->|Training| RF
    
    Sim -.->|Streamed Demo Data| API

Loading

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📱 Dashboard Features

Energy Executive Dashboard (/executive/dashboard)

Panel	Description
KPI Cards	Total demand, operational cost, grid efficiency, consumers served
Demand Trend	Area chart showing forecast vs actual monthly demand
Power Source Mix	Pie chart of current generation by source
Renewable Integration	Line chart of solar/wind performance over time
Cost Breakdown	Bar chart of operational cost categories

Grid Operator Dashboard (/operator/dashboard)

Panel	Description
Status Indicators	Grid frequency, voltage, battery status, AI system status
Power Sources	Real-time output from solar, wind, hydro, nuclear, coal, gas
Demand Forecast	Line chart showing next 5 hours prediction
AI Decision	Recommended action with amount and reasoning
AI Explanation	Bar chart showing feature importance
Scenario Simulator	Interactive sliders for temperature, solar, load, hour

Consumer Dashboard (/consumer/dashboard)

Panel	Description
Current Usage	Real-time power consumption and cost per hour
Monthly Cost	Total cost and kWh used this month
Savings	Amount saved vs average
Green Energy	Percentage of renewable sources used
Daily Usage	Line chart of weekly consumption pattern
Appliance Breakdown	Bar chart of energy usage by category
Peak Hours	List of highest usage time periods
Billing	Current balance and due date

Regulator Dashboard (/regulator/dashboard)

Panel	Description
Compliance Score	Overall compliance percentage
Grid Uptime	Reliability percentage
Safety Incidents	Number of incidents this quarter
Reports Due	Number of pending reports
Compliance Trend	Line chart of monthly compliance scores
Reliability Metrics	SAIDI, SAIFI, CAIDI, ASAI metrics
Compliance Standards	Status of safety, environmental, grid reliability standards
Alerts	Recent compliance and safety notifications

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📊 API Endpoints

Endpoint	Method	Description
/predict	POST	Predict energy demand
/decision	POST	Get AI decision recommendation
/explain	GET	Get feature importance
/simulate	POST	Run scenario simulation
/data/sample	GET	Get sample energy data
/data/power-sources	GET	Get power source data
/data/executive	GET	Get executive dashboard data
/data/operator	GET	Get operator dashboard data
/data/consumer	GET	Get consumer dashboard data
/data/regulator	GET	Get regulator dashboard data
/status	GET	API status

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🔬 Example Request & Response

Request

POST /decision
{
  "hour": 18,
  "day_of_week": 3,
  "temperature": 28,
  "solar_output": 2.5,
  "current_load": 10
}

Response

{
  "predicted_demand": 12.7,
  "decision": {
    "action": "battery_discharge",
    "amount": 2.1,
    "unit": "MW",
    "reason": "High demand predicted (12.7 MW). Activate battery storage to reduce grid load."
  },
  "timestamp": "2026-04-09T10:30:00"
}

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📊 Project Stats

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🔐 Disclaimer

GridMind AI provides probabilistic predictions based on ML models. For production deployment, integrate real-time sensor data and grid constraints.

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✍️ Endnote

⚡ Power the future of energy with autonomous AI decisions.

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🏷 Tags

ai energy smart-grid machine-learning fastapi nextjs react demand-forecasting decision-intelligence explainable-ai renewable-energy battery-optimization grid-management power-systems random-forest python typescript