Awesome

Datasets

• SC data: the dataset contains power grid series of 133 locations, and the location index, date, hour, temperature, precipitation, active power and reactive power is reported in the dataset.

Usage

• For CMU data:

  python NeuCast_CMU.py --method sar --day_pred 5 --end 23

  • method: the smoothing method used in the algorithm, including 'sar','ar','hw'
  • day_pred: control the predict length of the algorithm.
  • end: control the end day of the dataset.

• For SC data:

  python NeuCast_SC.py --loc_id 0 --method hw

  • loc_id: the location id, from 0~132
  • method: the smoothing method used in the algorithm, including 'sar','ar','hw'

Require

• Keras (2.0.9) with tensorflow backend
• rpy2 (2.9.1)

Reference

If you use the data or the NeuCast algorithom, please cite our work.

@inproceedings{neu2018,
  title={NeuCast: Seasonal Neural Forecast of Power Grid Time Series},
  author={Chen, Pudi and Liu, Shenghua and Shi, Chuan and Bryan Hooi and Wang, bai and Cheng, Xueqi},
  booktitle={International Joint Conference on Artificial Intelligence},
  year={2018},
}