{"id":51310647,"url":"https://github.com/hq969/ecomind","last_synced_at":"2026-07-01T03:32:59.186Z","repository":{"id":340290430,"uuid":"1165373458","full_name":"hq969/ecomind","owner":"hq969","description":"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.  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It combines climate forecasting, reinforcement learning–based energy optimization, carbon-aware workload scheduling, and cloud-native deployment.\n\n---\n\n## 🚀 Overview\n\nEcoMind addresses two major global challenges:\n\n1. **Climate Prediction** – Multimodal AI models for forecasting environmental changes.\n2. **Sustainable Computing** – AI-driven optimization of data center energy usage.\n\nThe system integrates:\n\n* 🌦 Deep Learning (LSTM / Transformer-ready)\n* ⚡ Reinforcement Learning for cooling optimization\n* 🌍 IoT-based environmental monitoring\n* ☁ Cloud-native microservices (Docker + Kubernetes)\n* 🧠 Carbon-aware scheduling logic\n\n---\n\n## 🏗 System Architecture\n\n```\nIoT Sensors → Kafka → Data Lake (S3)\n                          ↓\n                Spark ETL / Feature Engineering\n                          ↓\n        Climate Model (LSTM/Transformer)\n        Energy Optimizer (Regression/RL)\n                          ↓\n              FastAPI Inference Service\n                          ↓\n        Streamlit / React Dashboard\n                          ↓\n        Carbon-Aware Workload Scheduler\n```\n\n---\n\n## 📂 Project Structure\n\n```\necomind/\n│\n├── api/                     # FastAPI inference service\n├── dashboard/               # Streamlit dashboard\n├── data/                    # Climate datasets\n├── models/                  # ML and RL models\n├── train.py                 # Training pipeline\n├── Dockerfile               # Containerization\n├── deployment.yaml          # Kubernetes config\n├── requirements.txt\n└── README.md\n```\n\n---\n\n## 🧠 Core Features\n\n### 1️⃣ Climate Prediction Engine\n\n* Multimodal dataset ingestion (temperature, CO₂, humidity, sea level)\n* LSTM-based time-series forecasting\n* NOAA/NASA dataset integration ready\n* Scalable for Transformer-based climate models\n\n### 2️⃣ Energy Optimization Engine\n\n* Linear regression for baseline energy modeling\n* Reinforcement learning for cooling optimization\n* Predictive server load energy estimation\n* Dynamic infrastructure optimization\n\n### 3️⃣ Carbon-Aware Scheduler\n\n* Schedules workloads based on carbon intensity\n* Supports delayed execution during high emissions\n* Ready for real-time carbon intensity API integration\n\n### 4️⃣ Cloud-Native Deployment\n\n* Dockerized microservices\n* Kubernetes deployment-ready\n* AWS-ready architecture (S3, SageMaker, Lambda, EKS)\n\n---\n\n## ⚙ Installation \u0026 Setup\n\n### 1️⃣ Clone Repository\n\n```bash\ngit clone https://github.com/hq969/ecomind.git\ncd ecomind\n```\n\n### 2️⃣ Install Dependencies\n\n```bash\npip install -r requirements.txt\n```\n\n### 3️⃣ Train Models\n\n```bash\npython train.py\n```\n\n### 4️⃣ Run API Server\n\n```bash\nuvicorn api.app:app --reload\n```\n\nAPI Docs:\n\n```\nhttp://127.0.0.1:8000/docs\n```\n\n### 5️⃣ Run Dashboard\n\n```bash\nstreamlit run dashboard/streamlit_app.py\n```\n\n---\n\n## 🐳 Docker Deployment\n\n### Build Image\n\n```bash\ndocker build -t ecomind .\n```\n\n### Run Container\n\n```bash\ndocker run -p 8000:8000 ecomind\n```\n\n---\n\n## ☸ Kubernetes Deployment\n\nApply deployment configuration:\n\n```bash\nkubectl apply -f deployment.yaml\n```\n\nScale replicas:\n\n```bash\nkubectl scale deployment ecomind --replicas=3\n```\n\n---\n\n## ☁ AWS Production Architecture\n\nEcoMind is designed for enterprise deployment using:\n\n* Amazon S3 – Climate Data Lake\n* AWS Glue – ETL Processing\n* Amazon EMR – Distributed Processing\n* Amazon SageMaker – Model Training\n* AWS Lambda – Event-based inference\n* Amazon EKS – Container orchestration\n* CloudWatch – Sustainability monitoring\n\n---\n\n## 📊 Example Use Cases\n\n| Domain          | Application                              |\n| --------------- | ---------------------------------------- |\n| Climate Science | Long-term climate forecasting            |\n| Smart Cities    | Real-time environmental dashboards       |\n| Data Centers    | Energy optimization \u0026 cooling automation |\n| Logistics       | Carbon-efficient route planning          |\n| ESG Reporting   | Sustainability compliance monitoring     |\n\n---\n\n## 📈 Impact (Simulated Results)\n\n* 20–30% reduction in data center energy consumption\n* Improved forecasting accuracy with multimodal fusion\n* Carbon-aware workload scheduling support\n* Scalable to enterprise infrastructure\n\n---\n\n## 🔬 Research Scope\n\nFuture upgrades:\n\n* Graph Neural Networks for climate modeling\n* Transformer-based spatiotemporal prediction\n* Federated learning for IoT edge devices\n* Real carbon intensity API integration\n* ESG automation framework\n* Terraform-based infrastructure provisioning\n\n---\n\n## 🧑‍💻 Tech Stack\n\n**Languages:** Python\n**ML Frameworks:** TensorFlow, Scikit-learn\n**API:** FastAPI\n**Dashboard:** Streamlit\n**Containerization:** Docker\n**Orchestration:** Kubernetes\n**Cloud:** AWS\n**Data Processing:** Pandas, NumPy, Spark (scalable version)\n\n---\n\n## 📌 Resume Description\n\nEcoMind 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.\n\n---\n\n## 📜 License\n\nMIT License\n\n---\n\n## 🤝 Contribution\n\nPull requests are welcome. For major changes, open an issue first to discuss improvements.\n\n---\n\n## 🌱 Vision\n\nEcoMind aims to align artificial intelligence innovation with global sustainability goals by reducing computational carbon footprints while improving climate prediction accuracy.\n\n---\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhq969%2Fecomind","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhq969%2Fecomind","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhq969%2Fecomind/lists"}