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--- |
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title: Renewable Energy Potential Predictor |
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emoji: 🌤️ |
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colorFrom: green |
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colorTo: blue |
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sdk: gradio |
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sdk_version: 5.9.0 |
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app_file: app.py |
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pinned: false |
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--- |
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# Renewable Energy Potential Predictor |
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This Hugging Face Space provides an interactive interface for predicting wind and solar power potential based on satellite imagery and environmental data. |
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## How to Use |
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1. Upload the required images: |
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- RGB Satellite Image |
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- NDVI (Normalized Difference Vegetation Index) Image |
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- Terrain Map |
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- Elevation Data (as .npy file) |
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2. Enter weather parameters: |
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- Wind Speed (m/s) |
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- Wind Direction (degrees) |
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- Temperature (°C) |
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- Humidity (%) |
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3. Click "Submit" to generate predictions |
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The model will output two heatmaps showing the predicted wind and solar power potential for the given location. |
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## Input Requirements |
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- All images should be in RGB or grayscale format |
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- Elevation data should be a NumPy array saved as .npy file |
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- Weather parameters should be within reasonable ranges |
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## Example Data |
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The interface includes example data that you can use to test the model. Click "Run Example" to try it out. |
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## Model Details |
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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. |
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
