https://github.com/hq969/ecomind

EcoMind is an end-to-end AI-driven sustainability platform designed to address two critical global challenges: accurate climate forecasting and energy-efficient computing infrastructure. The system integrates multimodal climate datasets (temperature, CO₂, humidity, sea-level data) with deep learning models to generate predictive climate scenarios.
https://github.com/hq969/ecomind

kubernetes numpy pandas python reinforcement-learning tensorflow

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EcoMind is an end-to-end AI-driven sustainability platform designed to address two critical global challenges: accurate climate forecasting and energy-efficient computing infrastructure. The system integrates multimodal climate datasets (temperature, CO₂, humidity, sea-level data) with deep learning models to generate predictive climate scenarios.

• Host: GitHub
• URL: https://github.com/hq969/ecomind
• Owner: hq969
• License: mit
• Created: 2026-02-24T05:11:40.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2026-02-24T05:23:16.000Z (4 months ago)
• Last Synced: 2026-02-24T11:54:15.031Z (4 months ago)
• Topics: kubernetes, numpy, pandas, python, reinforcement-learning, tensorflow
• Language: Python
• Homepage:
• Size: 19.5 KB
• Stars: 1
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# 🌍 EcoMind – AI Models for Climate Prediction & Sustainable Computing

EcoMind is an end-to-end AI platform designed to model climate dynamics and optimize energy consumption in computing infrastructure. It combines climate forecasting, reinforcement learning–based energy optimization, carbon-aware workload scheduling, and cloud-native deployment.

---

## 🚀 Overview

EcoMind addresses two major global challenges:

1. **Climate Prediction** – Multimodal AI models for forecasting environmental changes.

2. **Sustainable Computing** – AI-driven optimization of data center energy usage.

The system integrates:

* 🌦 Deep Learning (LSTM / Transformer-ready)

* ⚡ Reinforcement Learning for cooling optimization

* 🌍 IoT-based environmental monitoring

* ☁ Cloud-native microservices (Docker + Kubernetes)

* 🧠 Carbon-aware scheduling logic

---

## 🏗 System Architecture

```

IoT Sensors → Kafka → Data Lake (S3)

                          ↓

                Spark ETL / Feature Engineering

                          ↓

        Climate Model (LSTM/Transformer)

        Energy Optimizer (Regression/RL)

                          ↓

              FastAPI Inference Service

                          ↓

        Streamlit / React Dashboard

                          ↓

        Carbon-Aware Workload Scheduler

```

---

## 📂 Project Structure

```

ecomind/

│

├── api/                     # FastAPI inference service

├── dashboard/               # Streamlit dashboard

├── data/                    # Climate datasets

├── models/                  # ML and RL models

├── train.py                 # Training pipeline

├── Dockerfile               # Containerization

├── deployment.yaml          # Kubernetes config

├── requirements.txt

└── README.md

```

---

## 🧠 Core Features

### 1️⃣ Climate Prediction Engine

* Multimodal dataset ingestion (temperature, CO₂, humidity, sea level)

* LSTM-based time-series forecasting

* NOAA/NASA dataset integration ready

* Scalable for Transformer-based climate models

### 2️⃣ Energy Optimization Engine

* Linear regression for baseline energy modeling

* Reinforcement learning for cooling optimization

* Predictive server load energy estimation

* Dynamic infrastructure optimization

### 3️⃣ Carbon-Aware Scheduler

* Schedules workloads based on carbon intensity

* Supports delayed execution during high emissions

* Ready for real-time carbon intensity API integration

### 4️⃣ Cloud-Native Deployment

* Dockerized microservices

* Kubernetes deployment-ready

* AWS-ready architecture (S3, SageMaker, Lambda, EKS)

---

## ⚙ Installation & Setup

### 1️⃣ Clone Repository

```bash

git clone https://github.com/hq969/ecomind.git

cd ecomind

```

### 2️⃣ Install Dependencies

```bash

pip install -r requirements.txt

```

### 3️⃣ Train Models

```bash

python train.py

```

### 4️⃣ Run API Server

```bash

uvicorn api.app:app --reload

```

API Docs:

```

http://127.0.0.1:8000/docs

```

### 5️⃣ Run Dashboard

```bash

streamlit run dashboard/streamlit_app.py

```

---

## 🐳 Docker Deployment

### Build Image

```bash

docker build -t ecomind .

```

### Run Container

```bash

docker run -p 8000:8000 ecomind

```

---

## ☸ Kubernetes Deployment

Apply deployment configuration:

```bash

kubectl apply -f deployment.yaml

```

Scale replicas:

```bash

kubectl scale deployment ecomind --replicas=3

```

---

## ☁ AWS Production Architecture

EcoMind is designed for enterprise deployment using:

* Amazon S3 – Climate Data Lake

* AWS Glue – ETL Processing

* Amazon EMR – Distributed Processing

* Amazon SageMaker – Model Training

* AWS Lambda – Event-based inference

* Amazon EKS – Container orchestration

* CloudWatch – Sustainability monitoring

---

## 📊 Example Use Cases

| Domain          | Application                              |

| --------------- | ---------------------------------------- |

| Climate Science | Long-term climate forecasting            |

| Smart Cities    | Real-time environmental dashboards       |

| Data Centers    | Energy optimization & cooling automation |

| Logistics       | Carbon-efficient route planning          |

| ESG Reporting   | Sustainability compliance monitoring     |

---

## 📈 Impact (Simulated Results)

* 20–30% reduction in data center energy consumption

* Improved forecasting accuracy with multimodal fusion

* Carbon-aware workload scheduling support

* Scalable to enterprise infrastructure

---

## 🔬 Research Scope

Future upgrades:

* Graph Neural Networks for climate modeling

* Transformer-based spatiotemporal prediction

* Federated learning for IoT edge devices

* Real carbon intensity API integration

* ESG automation framework

* Terraform-based infrastructure provisioning

---

## 🧑‍💻 Tech Stack

**Languages:** Python

**ML Frameworks:** TensorFlow, Scikit-learn

**API:** FastAPI

**Dashboard:** Streamlit

**Containerization:** Docker

**Orchestration:** Kubernetes

**Cloud:** AWS

**Data Processing:** Pandas, NumPy, Spark (scalable version)

---

## 📌 Resume Description

EcoMind is an AI-driven climate forecasting and sustainable computing platform leveraging LSTM-based time-series modeling and reinforcement learning for energy optimization. The system is containerized using Docker and deployed via Kubernetes, integrating AWS services for scalable AI infrastructure.

---

## 📜 License

MIT License

---

## 🤝 Contribution

Pull requests are welcome. For major changes, open an issue first to discuss improvements.

---

## 🌱 Vision

EcoMind aims to align artificial intelligence innovation with global sustainability goals by reducing computational carbon footprints while improving climate prediction accuracy.

---