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"CTRL: Collaborative Temporal Representation Learning for Wind Power Forecasting" - Official Implementation

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(EITCE'2024) CTRL: Collaborative Temporal Representation Learning for Wind Power Forecasting

The complete implementation of the CTRL model is provided in model_ctrl.py within this repository.

Updates

🚩 News (2025.08): CTRL has been included in [pyFAST]. For implementation details, see the CTRL model code in pyFAST's MTS module.

Overall Architecture

Model Architecture

Requirements

  • Python 3.10+
  • PyTorch 2.0.0+

Citation

If you find this work useful in your research, please use the following citation formats:

BibTeX formats:

@inproceedings{pro/eitce2024/Hu,
    author = {Yue Hu and Senzhen Wu and Yu Chen and Xinhao He and Zihao Xie and Zhijin Wang and Xiufeng Liu and Yonggang Fu},
    title = {{CTRL}: Collaborative Temporal Representation Learning for Day-ahead Wind Power Forecasting},
    booktitle = {Proceedings of the 8th International Conference on Electronic Information Technology and Computer Engineering},
    doi = {10.1145/3711129.3711336},
    publisher = {{ACM}},
    year = {2024},
    month = {10},
    address = {Haikou, China},
}

APA/Plain formats:

Yue Hu and Senzhen Wu and Yu Chen and Xinhao He and Zihao Xie and Zhijin Wang and Xiufeng Liu and Yonggang Fu. CTRL:
Collaborative Temporal Representation Learning for Wind Power Forecasting. ACM EITCE
2024, https://doi.org/10.1145/3711129.3711336

Acknowledgement

We appreciate the following GitHub repos a lot for their valuable code and efforts.

Contact

If you have any questions or suggestions, feel free to contact me at [email protected].

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"CTRL: Collaborative Temporal Representation Learning for Wind Power Forecasting" - Official Implementation

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