---
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.

![Solar Prediction Example](docs/BT202504010900-ddpm.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.