☀️ PV Energy Output Prediction

A web-based machine learning app that predicts photovoltaic (PV) solar energy output using weather features and trained regression models. Ideal for solar engineers, data scientists, and renewable energy analysts.

🔗 Live Demo: (https://predict-energy-aman-007.streamlit.app/) 📦 Built With: Streamlit, scikit-learn, joblib, pandas, XGBoost

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🚀 Features

• 📈 Predict solar energy output in real-time
• ☁️ Uses weather features like temperature, humidity, wind speed, and irradiation
• 🧠 Trained ML model using XGBoost for accurate forecasting
• 🖥️ Deployed as a lightweight web application using Streamlit

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📂 Project Structure

Predict_Energy/
│
├── app.py                  # Streamlit web application
├── pv_model.joblib         # Pretrained regression model (XGBoost)
├── requirements.txt        # Python dependencies
└── solarPower_50m.csv      # Sample dataset used for training/testing

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⚙️ How to Run

1. Clone the repository

git clone https://github.com/yourusername/Predict_Energy.git
cd Predict_Energy

2. Install dependencies

pip install -r requirements.txt

3. Launch the app

streamlit run app.py

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📊 Sample Input Features

• Temperature (°C)
• Humidity (%)
• Wind Speed (m/s)
• Solar Irradiance (W/m²)

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📦 Model Training (Optional)

If you wish to retrain the model:

# Load data
df = pd.read_csv("solarPower_50m.csv")

# Define features/target and train with XGBoost
...

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👤 Author

Amanpreet Ahluwalia Portfolio | LinkedIn

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