🌬️ Wind Power Generation Prediction | Random Forest

A machine learning project that forecasts wind power generation across multiple locations using the Random Forest algorithm.

---

📌 About The Project

This project analyzes wind energy data collected from 4 different locations to build a predictive model that accurately forecasts wind power generation. It covers the complete data science pipeline from raw data to model predictions.

---

🛠️ Tech Stack

---

📂 Project Structure

Wind-Power-Generation-Forest/
├── Location1.csv              # Wind data - Location 1
├── Location2.csv              # Wind data - Location 2
├── Location3.csv              # Wind data - Location 3
├── Location4.csv              # Wind data - Location 4
├── merge_locations.csv        # Combined dataset of all locations
├── Wind-Forecasting.ipynb     # Main Jupyter Notebook
└── README.md

---

🔍 Project Workflow

1. Data Collection - Wind energy data from 4 locations
2. Data Merging - Combined all location datasets into one
3. Data Cleaning - Handling missing values and outliers
4. EDA - Exploratory Data Analysis and visualizations
5. Feature Engineering - Selecting key features
6. Model Building - Random Forest Regressor
7. Model Evaluation - Accuracy and performance metrics
8. Forecasting - Final wind power predictions

---

📊 Dataset Info

File	Description
Location1.csv	Wind data from Location 1
Location2.csv	Wind data from Location 2
Location3.csv	Wind data from Location 3
Location4.csv	Wind data from Location 4
merge_locations.csv	All locations merged into one dataset

---

🤖 Model Used

Model	Type	Purpose
Random Forest Regressor	Ensemble Learning	Wind Power Forecasting

---

📈 Key Results

• Successfully predicted wind power generation across 4 locations
• Merged and analyzed multi-location wind datasets
• Identified key weather features influencing power output
• Built an accurate forecasting model using Random Forest
• Visualized wind speed vs power generation trends

---

💡 Key Learnings

• Multi-location data merging and preprocessing
• Time series and energy data analysis
• Random Forest model building and hyperparameter tuning
• Feature importance analysis in energy forecasting
• Renewable energy domain understanding

---

🚀 How To Run This Project

# Step 1 - Clone the repository
git clone https://github.com/Ashwin14101/Wind-Power-Generation-Forest.git

# Step 2 - Go to the project folder
cd Wind-Power-Generation-Forest

# Step 3 - Install required libraries
pip install pandas numpy scikit-learn matplotlib seaborn jupyter

# Step 4 - Open Jupyter Notebook
jupyter notebook Wind-Forecasting.ipynb

---

📦 Libraries Required

pandas
numpy
scikit-learn
matplotlib
seaborn
jupyter

---

🌱 Domain

Renewable Energy | Machine Learning | Data Science | Forecasting

---

👨‍💻 Author

Ashwin

---

⭐ If you found this project useful, consider giving it a star!