Wind Power Prediction is a machine learning-based application developed to estimate the power generated by a wind turbine using environmental and operational parameters such as wind speed, wind direction, temperature, pressure, air density, rotor RPM, and blade pitch angle.
The project applies data preprocessing, feature engineering, and regression algorithms to accurately predict wind power output. A trained machine learning model is integrated with a Flask web application, allowing users to enter turbine parameters through a user-friendly interface and receive real-time power predictions.
- Predicts wind turbine power output using Machine Learning
- Interactive Flask-based web application
- Data preprocessing and feature engineering
- Multiple regression models for performance comparison
- Real-time prediction interface
- Easy deployment and scalability
- Python
- Flask
- Pandas
- NumPy
- Scikit-learn
- Matplotlib
- HTML
- CSS
- JavaScript
- Data Collection
- Data Cleaning
- Feature Engineering
- Model Training
- Model Evaluation
- Real-Time Prediction
- Web Deployment
- Renewable energy forecasting
- Smart grid management
- Wind farm performance monitoring
- Energy production optimization
- Sustainable energy planning
- Deep Learning models (LSTM, GRU)
- Live weather API integration
- Power BI dashboard
- Cloud deployment
- Mobile-friendly interface
Shankar Koujalagi
Bachelor of Technology (CSE - AIML)
Dayananda Sagar University
This project is developed for academic and educational purposes.