phious/Renewable-Energy-Optimization

Project Idea: Building an AI system that optimizes the generation and distribution of renewable energy in African communities. The tool could predict energy demands, manage energy storage, and ensure efficient use of resources like solar and wind power. ### README.md # Renewable Energy Optimization ## Purpose The Renewable Energy Optimization project aims to build an AI system to optimize the generation and distribution of renewable energy in African communities. This tool will predict energy demands, manage energy storage, and ensure efficient use of resources like solar and wind power. By leveraging advanced machine learning and optimization techniques, we seek to create a more sustainable and efficient energy infrastructure that can adapt to the unique challenges faced by these communities. ## Project Structure The project is organized into the following main directories: - **backend**: Contains all server-side code, including API endpoints and database interactions. - **frontend**: Contains all client-side code, including the user interface and front-end logic. - **models**: Contains machine learning models for energy demand prediction and optimization algorithms. - **data**: Contains datasets used for training and testing the machine learning models. - **docs**: Contains documentation related to the project, including setup instructions and API documentation. ### Directory Breakdown - **backend/** - **app.py**: Main application file for running the Flask server. - **routes/**: Directory containing route handlers for API endpoints. - **models/**: Directory containing ORM models for database schemas. - **config/**: Configuration files for the application. - **frontend/** - **src/**: Main source directory for the React application. - **components/**: Directory containing React components. - **services/**: Directory containing services for API calls. - **styles/**: Directory containing CSS and styling files. - **models/** - **energy_demand_model.py**: Machine learning model for predicting energy demand. - **storage_management_model.py**: Optimization algorithm for managing energy storage. - **utils/**: Utility functions and helpers for model training and evaluation. - **data/** - **raw/**: Raw datasets. …