Project Overview

This repository contains a machine learning solution for optimizing solar energy usage and reducing grid costs in private households. The main objective is to maximize the utilization of solar energy through a smart battery optimizer, minimizing reliance on the grid and reducing electricity bills. The system dynamically adjusts the charging and discharging of the battery based on energy demand, solar generation, and grid prices.

Data

The data used in this repository can be found on the SMARD.de database.

Key Features

• Intelligent battery optimization for maximizing solar energy usage
• Real-time monitoring of energy demand, solar generation, and grid prices
• Automatic adjustment of battery charging and discharging based on energy dynamics
• Cost savings by minimizing reliance on the grid and optimizing battery usage
• Reduction of carbon footprint through increased use of clean energy sources
• Reliable and sustainable energy supply for private households

Getting Started

• Clone the repository and install the required dependencies by running pip install -r requirements.txt

• Configure the system settings and parameters according to your specific setup.

• Train the machine learning models using historical data or pre-trained models.

• Integrate the solution with your solar energy system and smart battery storage.

• Monitor the system performance and track cost savings through the provided dashboard.

• Customize the solution based on your energy usage patterns and preferences.

Contributing

Contributions from the data science community are encouraged. You can contribute by improving the machine learning models, enhancing the optimization algorithm, or suggesting new features. Feel free to submit your contributions through pull requests to collaborate on creating a more efficient and cost-effective energy storage system.

Future Enhancements

• Integration with smart home automation systems for seamless energy management.
• Improving the performance of the forecasting models.
• Diversify energy sources: Integrate wind power alongside solar energy.
• Integration with broader energy market APIs for real-time pricing and dynamic optimization.
• Deep Q-learning approach as replacement for linear programming.
• Monetizing excess energy via grid integration.

We are excited about the potential of these enhancements to further reduce grid costs, promote clean energy usage, and improve energy efficiency. Together, we can contribute to a more sustainable and economically viable energy landscape