DGMR_SolRad

DGMR Solar Radiation Nowcasting ☀️

A deep learning model for solar radiation nowcasting using modified Deep Generative Model of Rainfall (DGMR) 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.

Overview

This repository implements two model variants for solar radiation forecasting:

• DGMR_SO: Full Deep Generative Models with one generator and two discriminators during the training stage
• Generator_only: Only one generator during the training stage

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
• Time features: Temporal sin/cos encoding for day and hour

Installation

1. Clone the repository & install Git LFS:

git lfs install
git clone <repository-url>
cd DGMR_SolRad
git lfs pull
git lfs ls-files # confirm whether models weights & sample data are downloaded

1. Install dependencies:

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:

python inference.py --model-type DGMR_SO --basetime 202504131100

Command Line Arguments

• --model-type: Choose between DGMR_SO or Generator_only (default: DGMR_SO)
• --basetime: Timestamp for input data in format YYYYMMDDHHMM (default: 202504131100)

Example

# Using DGMR_SO model
python inference.py --model-type DGMR_SO --basetime 202504131100

# Using Generator-only model
python inference.py --model-type Generator_only --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/DGMR_SO/ft36/weights.ckpt
• model_weights/Generator_only/ft36/weights.ckpt

License

This project is released under the MIT License.