https://github.com/mainakverse/wind-energy-predictor
WindPro is an advanced AI-powered wind energy prediction platform that leverages machine learning to forecast wind energy generation with unprecedented accuracy and reliability.
https://github.com/mainakverse/wind-energy-predictor
folium-maps streamlit-webapp wind-energy-analytics
Last synced: 3 months ago
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WindPro is an advanced AI-powered wind energy prediction platform that leverages machine learning to forecast wind energy generation with unprecedented accuracy and reliability.
- Host: GitHub
- URL: https://github.com/mainakverse/wind-energy-predictor
- Owner: MainakVerse
- Created: 2025-03-05T01:36:01.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-03-09T03:55:26.000Z (7 months ago)
- Last Synced: 2025-03-12T19:37:52.541Z (7 months ago)
- Topics: folium-maps, streamlit-webapp, wind-energy-analytics
- Language: Python
- Homepage: https://wind-energy-predictor.streamlit.app/
- Size: 1.82 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Wind Power Forecasting Application
## Overview
This project involves the development and deployment of a **wind power forecasting application** leveraging **machine learning** and **deep learning** techniques. The application predicts wind power using key variables such as wind speed, wind direction, and theoretical power. A user-friendly web interface was built using **Streamlit** for real-time predictions.
---
## Features
- **Machine Learning Models**:
- Implemented models like Linear Regression, XGBoost, and Ensemble methods.
- Achieved **R² = 0.9667** with effective hyperparameter tuning and cross-validation.
- **Deep Learning Models**:
- Developed and optimized neural network architectures:
- Single Hidden Layer.
- Multiple Hidden Layers.
- Long Short-Term Memory (LSTM) networks.
- Best LSTM model achieved **R² = 0.92**.
- **Time Series Analysis**:
- Incorporated temporal dependencies to improve prediction accuracy.
- **Feature Engineering**:
- Conducted advanced preprocessing, including:
- Multivariate signal decomposition.
- Variable selection.
- **Web Application**:
- Designed a user-friendly interface using **Streamlit**.
- Integrated machine learning models for real-time wind power forecasts.
---
## Technology Stack
- **Programming Languages**: Python
- **Machine Learning Libraries**: scikit-learn, XGBoost
- **Deep Learning Libraries**: TensorFlow/Keras, PyTorch
- **Web Framework**: Streamlit
---
## Results
- **Machine Learning Performance**:
- Achieved high prediction accuracy with Linear Regression and XGBoost (**R² = 0.9667**).
- **Deep Learning Performance**:
- Best LSTM model demonstrated robust predictive capabilities (**R² = 0.92**).
---
## How to Run
1. **Clone the Repository**:
```bash
git clone https://github.com/username/wind-power-forecasting.git
cd wind-power-forecasting
```
2. **Install Dependencies**:
```bash
pip install -r requirements.txt
```
3. **Run the Streamlit Application**:
```bash
streamlit run app.py
```
---
## Dataset
The dataset contains key variables for wind power forecasting, such as:
- Wind Speed
- Wind Direction
- Theoretical Power
The dataset was preprocessed to handle missing values, outliers
---
## Skills Highlighted
- Machine Learning (Linear Regression, XGBoost, Ensemble Methods)
- Deep Learning (Neural Networks, LSTM)
- Time Series Analysis
- Streamlit Web Application Development
- Feature Engineering
---