Contact me with [email protected].

The official code for paper [A Unifying Framework of Attention-based Neural Load Forecasting]

We propose a unifying deep learning framework for short-term load forecasting. Our novel approach includes time-varying feature weighting, hierarchical temporal attention, and feature-reinforced error correction. The proposed modular design features good generalization capability, which achieves superior performance over the SOTA.

Run load forecasting model training, update model training and test result for ISO-NE dataset:

python ANLFF.py --data ISONE --final_run --updateCkpName checkpoint_update_ISO --logname ANLFF_ISO --subName ANLFF_ISO

Run load forecasting model training, update model training and test result for NAU dataset:

python ANLFF.py --data Utility --final_run --updateCkpName checkpoint_update_NAU --logname ANLFF_NAU --subName ANLFF_NAU

The project is only free for academic research purposes, but needs authorization for commerce. For commerce permission, please contact [email protected].

If you use our code/model, please cite our [paper].

@ARTICLE{10122506,
  author={Xiong, Jing and Zhang, Yu},
  journal={IEEE Access}, 
  title={A Unifying Framework of Attention-Based Neural Load Forecasting}, 
  year={2023},
  volume={11},
  number={},
  pages={51606-51616},
  doi={10.1109/ACCESS.2023.3275095}}
}