Project Title: Solar Panel Optimization using Machine Learning

This project presents an innovative approach to optimizing solar panel tilt angles using machine learning techniques. The goal is to maximize energy generation efficiency by accurately predicting optimal tilt angles based on various environmental factors.

Key Features:

1. Utilizes historical solar irradiance data, weather conditions, and geographic information
   
2. Implements multiple machine learning algorithms including Linear Regression, Random Forest, Decision Tree, XGBoost, and LSTM
   
3. Provides hourly, daily, and monthly optimal angle predictions for PV systems
   
4. Focuses on Ahmedabad City (latitude 23.03, longitude 72.59) as a case study
   
5. Integrates with NASA POWER dataset for comprehensive environmental inputs

Technical Highlights:

1. Evaluates model performance using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE)
   
2. LSTM model outperforms other algorithms in test dataset predictions
   
3. Demonstrates significant variability in optimal tilt angles across different time scales

Potential Impact:

1. Aims to increase PV system effectiveness by up to 30% compared to fixed-angle installations
   
2. Offers a user-friendly web interface for real-time predictions, beneficial for both users and stakeholders
   
3. Contributes to the advancement of sustainable energy practices and cost-efficient solar solutions

Future Scope:

1. Integration with real-time weather forecasts for dynamic adjustments
   
2. Development of hardware systems for automatic panel positioning
   
3. Exploration of advanced tracking algorithms and remote monitoring capabilities

This project showcases the application of machine learning in renewable energy optimization, offering a scalable solution to enhance solar energy efficiency and accessibility.