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--- |
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license: mit |
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tags: |
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- solar-radiation |
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- deep-learning |
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- nowcasting |
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- dgmr |
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--- |
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# DGMR Solar Radiation Nowcasting ☀️ |
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A deep learning model for solar radiation nowcasting using modified [Deep Generative Model of Rainfall (DGMR)](https://www.nature.com/articles/s41586-021-03854-z) architecture with Solar radiation Output (DGMR-SO). The model predicts clearsky index and converts it to solar radiation for up to 36 time steps ahead. |
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## Overview |
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This repository implements two model variants for solar radiation forecasting: |
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- **DGMR_SO**: Full Deep Generative Models with one generator and two discriminators during the training stage |
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- **Generator_only**: Only one generator during the training stage |
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The model uses multiple input sources: |
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- **Himawari satellite data**: Clearsky index calculated from Himawari satellite data |
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- **WRF Prediction**: Clearsky index from WRF's solar irradiation prediction |
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- **Topography**: Static topographical features |
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- **Time features**: Temporal sin/cos encoding for day and hour |
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## Installation |
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1. Clone the repository & install Git LFS: |
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```bash |
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git lfs install |
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git clone <repository-url> |
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cd DGMR_SolRad |
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git lfs pull |
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git lfs ls-files # confirm whether models weights & sample data are downloaded |
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``` |
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2. Install dependencies: |
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```bash |
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pip install -r requirements.txt |
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``` |
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## Requirements |
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- Python 3.x |
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- PyTorch 2.4.0 |
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- NumPy 1.26.4 |
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- einops 0.8.0 |
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## Usage |
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### Basic Inference |
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Run solar radiation prediction using the pre-trained models: |
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```bash |
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python inference.py --model-type DGMR_SO --basetime 202504131100 |
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``` |
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### Command Line Arguments |
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- `--model-type`: Choose between `DGMR_SO` or `Generator_only` (default: `DGMR_SO`) |
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- `--basetime`: Timestamp for input data in format YYYYMMDDHHMM (default: `202504131100`) |
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### Example |
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```bash |
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# Using DGMR_SO model |
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python inference.py --model-type DGMR_SO --basetime 202504131100 |
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# Using Generator-only model |
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python inference.py --model-type Generator_only --basetime 202507151200 |
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``` |
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## Sample Data |
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The repository includes sample data files: |
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- `sample_202504131100.npz` |
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- `sample_202504161200.npz` |
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- `sample_202507151200.npz` |
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## Model Weights |
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Pre-trained weights are available for both models: |
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- `model_weights/DGMR_SO/ft36/weights.ckpt` |
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- `model_weights/Generator_only/ft36/weights.ckpt` |
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## License |
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This project is released under the MIT License. |
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