Transplit: fast and cheaper energy demand forecasting

Native installation

Requirements

• Python 3.8+
• Pytorch 1.10+

Optional

• PyTorch installed with a GPU to run it faster ;)

You can create a virtual environment with conda:

conda create -n transplit python=3.8

Install dependencies with:

pip install -r requirements.txt

Docker usage

If you don't want to install it natively and want an easy solution, you can use Docker.

First pull the Nvidia's PyTorch image:

docker pull nvcr.io/nvidia/pytorch:23.02-py3

If you want to run the container with GPU, you will need to setup Docker for it by installing the nvidia-container-runtime and nvidia-container-toolkit packages.

Then run the container from this directory directory:

docker run -it --rm --gpus all --name transplit -v $(pwd):/workspace nvcr.io/nvidia/pytorch:23.02-py3

Once in it, install the dependencies with:

pip install -r requirements.txt

(already done in the Dockerfile, if you used it)

And download the data if not already done.

Run one experiment

To run one experiment:

python run.py --data_path datasets/creos.csv --pred_len 168

Dataset format

Main dataset
The dataset should be a csv file with named columns (on the first line), with the first column named date, containing the timestamps. Every other column is considered as a separate consumption time series (e.g. a single consumer). Example:

date,consumer1,consumer2,consumer3
2020-01-01 00:00:00,0.1,0.2,0.3
2020-01-01 00:15:00,0.2,0.3,0.4
2020-01-01 00:30:00,0.3,0.4,0.5
...

External factors (optional)
Some other features might be common to all consumers, such as the temperature, holidays, etc. These features should be in a separate csv file, with the same format as the main dataset.

date,temperature,holiday
2020-01-01 00:00:00,10.0,0
2020-01-01 00:15:00,10.1,0
2020-01-01 00:30:00,10.2,0
...

float and categorical features are distinguished by their type (float or int).

Note 1: The timestamps should be the same in both files.

Note 2: For now, on this model, external factors are not efficient. Important changes to take them and the consumer's profile into account are currently under experiment for a new contribution.

Model options

All options are listed in the config.py file. You can run the script with the --help option to see them.
However, most of them are not intended to change; you might want to change the following ones:

• --data_path: dataset (csv) to use
• --external_factors: external factors dataset (csv) to use
• --seq_len: input sequence length (default: 336)
• --pred_len: prediction length (default: 168)
• --period: number of samples within one season (default: 24)
• --batch_size: batch size (default: 32)
  • Note: modifying the batch size can alter the results; but in general, a higher batch size allows to train faster
• --train_ratio: ratio of the dataset to use for training (default: 0.8 = 80%)
• --test_ratio: ratio of the dataset to use for testing (default: 0.2 = 20%)
  • Note: the remaining ratio is used for validation, if needed
• --loss: loss function to use (default: mse)
  • Other losses: mae, huber, adaptive
  • Note: if needed, you can easily implement your own loss in exp/exp_main.py, _select_criterion function

Reuse a trained model

from models.transplit import Model
from config import get_config

args = get_config(
    pred_len=168,
    batch_size=32
)
model = Model(args)
model.load_model('checkpoints/path/model.pth')

# important: the model works with standardized data
output = model(
    'x_enc' = torch.rand(32, 336, 1),
    'x_mark_enc' = torch.zeros((32, 168, 4), dtype=int)
)
# output.shape: (32, 168, 1)

x_mark_enc has 4 channels: month, day, weekday, hour, respectively ranging from 0 to 11, 30, 6, 23. Additional float or categorical features (if used during the training) should be concatenated to x_enc and x_mark_enc before passing it to the model.

Acknowledgement

We acknowledge the following github repositories that made the base of our work:

https://github.com/zhouhaoyi/Informer2020
https://github.com/AaltoML/generative-inverse-heat-dissipation
https://github.com/BorealisAI/scaleformer
https://github.com/jonbarron/robust_loss_pytorch.git
https://github.com/Nixtla/neuralforecast