|
|
--- |
|
|
license: mit |
|
|
tags: |
|
|
- solar-radiation |
|
|
- deep-learning |
|
|
- nowcasting |
|
|
- ddpm |
|
|
- MCVD |
|
|
--- |
|
|
|
|
|
# DDPM Solar Radiation model |
|
|
A deep learning model for solar radiation nowcasting using modified [MCVD](https://arxiv.org/pdf/2205.09853) model, a kind of DDPM model for video generation. The model predicts clearsky index and converts it to solar radiation for up to 6 or 36 time steps ahead. |
|
|
|
|
|
<br> |
|
|
|
|
|
Below is an example of DDPM generation process for 1-hour solar radiation prediction (6 time steps). The total iteration is 1000 steps, and every 50 steps are shown in the gif. |
|
|
|
|
|
 |
|
|
|
|
|
## Overview |
|
|
|
|
|
This repository contains two trained models (1hr & 6hr) for solar radiation forecasting: |
|
|
- **1hr DDPM Model**: Predicts solar radiation up to 1 hour ahead (6 time steps) |
|
|
- **6hr DDPM Model**: Predicts solar radiation up to 6 hours ahead (36 time steps). |
|
|
|
|
|
The model uses multiple input sources: |
|
|
- **Himawari satellite data**: Clearsky index calculated from Himawari satellite data |
|
|
- **WRF Prediction**: Clearsky index from WRF's solar irradiation prediction |
|
|
- **Topography**: Static topographical features |
|
|
|
|
|
## Installation |
|
|
|
|
|
1. Clone the repository & install Git LFS: |
|
|
```bash |
|
|
git lfs install |
|
|
git clone <repository-url> |
|
|
cd Diffusion_SolRad |
|
|
git lfs pull |
|
|
git lfs ls-files # confirm whether models weights & sample data are downloaded |
|
|
``` |
|
|
|
|
|
2. Install dependencies: |
|
|
```bash |
|
|
pip install -r requirements.txt |
|
|
``` |
|
|
|
|
|
## Requirements |
|
|
|
|
|
- Python 3.x |
|
|
- PyTorch 2.4.0 |
|
|
- NumPy 1.26.4 |
|
|
- einops 0.8.0 |
|
|
|
|
|
## Usage |
|
|
|
|
|
### Basic Inference |
|
|
|
|
|
Run solar radiation prediction using the pre-trained models: |
|
|
|
|
|
```bash |
|
|
python inference.py --pred-hr [1hr/6hr] --pred-mode [DDPM/DDIM] --basetime 202504131100 |
|
|
``` |
|
|
|
|
|
### Command Line Arguments |
|
|
|
|
|
- `pred-mode`: Choose between `DDPM` or `DDIM` sampling methods (default: `DDPM`) |
|
|
- `pred-hr`: Choose between `1hr` or `6hr` prediction models (default: `1hr`) |
|
|
- `--basetime`: Timestamp for input data in format YYYYMMDDHHMM (default: `202504131100`) |
|
|
|
|
|
### Example |
|
|
|
|
|
```bash |
|
|
# DDIM sampling method for 1-hour prediction |
|
|
python inference.py --pred-hr 1hr --pred-mode DDIM --basetime 202507151200 |
|
|
``` |
|
|
|
|
|
## Sample Data |
|
|
|
|
|
The repository includes sample data files: |
|
|
- `sample_202504131100.npz` |
|
|
- `sample_202504161200.npz` |
|
|
- `sample_202507151200.npz` |
|
|
|
|
|
## Model Weights |
|
|
|
|
|
Pre-trained weights are available for both models: |
|
|
- `model_weights/ft06_01hr/weights.ckpt` |
|
|
- `model_weights/ft36_06hr/weights.ckpt` |
|
|
|
|
|
## License |
|
|
|
|
|
This project is released under the MIT License. |
|
|
|
