EcoPort 🍀

About 📘

Ecoport is a synergistic eco-friendly transformation portal that leverages the power of data, AI, and IoT Systems.

1. Ecofleet Dashboard 💡

Ecoport.Demo._.Dashboard.-.Google.Chrome.2024-10-12.20-52-52.online-video-cutter.com.mp4

A real-time monitoring and AI-driven solution designed to optimize energy consumption and reduce carbon emissions in port operations. By integrating IoT sensors, machine learning models, and an interactive dashboard, this system provides actionable insights into energy usage and recommends strategies to reduce environmental impact while maintaining operational efficiency.

2. Smart Energy Monitoring System ⚡

Ecoport_.Dashboards.-.Google.Chrome.2024-10-12.20-54-31.online-video-cutter.com.1.mp4

A smart energy management system that uses AI to forecast energy consumption and optimize energy usage in real-time. It has three key features: temperature & humidity monitoring, fuel monitoring, and fleet tracking.

Installation

Prerequisites

• Python 3.9+
• Node.js 14+
• Docker (for local containerization)
• PostgreSQL (for database setup)
• Kubernetes Engine API
• Google Container Registry API
• Google Cloud SDK
• Kubectl

Clone the Repository

git clone https://github.com/Irisss142/Binjai-Kingdom.git
cd Binjai-Kingdom

Environment Variables

Create a .env file in the root directory to store your environment variables:

DATABASE_URL=your_database_url
SECRET_KEY=your_secret_key

Backend Setup

Install Python Dependencies

Navigate to the /backend directory and install the required dependencies:

cd backend
pip install -r requirements.txt

Initialize the Database

Ensure your database is running and initialize it:

python database/init_db.py

Run the Backend Server

python api/app.py

The API should now be running at http://localhost:5000.

Frontend Setup

Install Frontend Dependencies

Navigate to the /frontend directory and install the required dependencies:

cd frontend
npm install

Run the Frontend

npm start

The frontend should now be running at http://localhost:3000.

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AI Model

Training the AI Model

Ensure your historical energy data is stored in the /data folder. Train the AI model with the following command:

python ai_model/train.py

Running Predictions

You can use the trained model to predict future energy usage:

python ai_model/predict.py

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Deployment

Docker Compose (Local Deployment)

You can run the entire system (backend, frontend, and database) locally using Docker Compose:

docker-compose up --build

Cloud Deployment

For cloud deployment, use Kubernetes or Google Cloud Run. Follow the instructions in the infrastructure/ folder:

• Kubernetes Deployment: Use the k8s-deployment.yaml for Kubernetes.
• Google Cloud Run: Use the cloud_run_deploy.sh script for Google Cloud deployment.

Usage

Real-Time Monitoring

1. Access the dashboard via http://localhost:3000.
2. View real-time energy consumption and emissions data.
3. Receive AI-driven recommendations for optimizing energy usage.

Forecasting Emissions

• Use the AI model to forecast future energy usage and carbon emissions.
• Visualize predicted trends in the dashboard.

AI Recommendations

• Access energy-saving recommendations in the dashboard.
• Apply suggestions to reduce energy consumption during peak operational hours.

Tech Stack

Backend

• Python 3.9+
• FastAPI
• PostgreSQL
• SQLAlchemy
• Redis & Celery
• Docker
• Google Kubernetes Engine (GKE)

Frontend

• React
• Plotly.js
• npm & Webpack
• Tailwind CSS
• Docker

Machine Learning

• TensorFlow/Keras
• Scikit-learn, Pandas, NumPy

Cloud & Deployment

• Google Cloud Platform (GCP)
• Kubernetes Secrets