---
title: Renewable Energy Potential Predictor
emoji: 🌤️
colorFrom: green
colorTo: blue
sdk: gradio
sdk_version: 5.9.0
app_file: app.py
pinned: false
---

# Renewable Energy Potential Predictor

This Hugging Face Space provides an interactive interface for predicting wind and solar power potential based on satellite imagery and environmental data.

## How to Use

1. Upload the required images:
   - RGB Satellite Image
   - NDVI (Normalized Difference Vegetation Index) Image
   - Terrain Map
   - Elevation Data (as .npy file)

2. Enter weather parameters:
   - Wind Speed (m/s)
   - Wind Direction (degrees)
   - Temperature (°C)
   - Humidity (%)

3. Click "Submit" to generate predictions

The model will output two heatmaps showing the predicted wind and solar power potential for the given location.

## Input Requirements

- All images should be in RGB or grayscale format
- Elevation data should be a NumPy array saved as .npy file
- Weather parameters should be within reasonable ranges

## Example Data

The interface includes example data that you can use to test the model. Click "Run Example" to try it out.

## Model Details

The predictor uses a deep learning model trained on satellite imagery and environmental data to estimate renewable energy potential. The model architecture combines CNN-based image processing with weather data integration for comprehensive predictions.

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference