Acknowledgements

This project is part of Samhan's PhD study, supported by the University International Postgraduate Award (UIPA) Scholarship from UNSW, the Industry Collaboration Project Scholarship from Ausgrid, and the RACE for 2030 Scholarship. We also acknowledge Solcast and the Australian Bureau of Meteorology (BOM) for providing access to historical weather datasets for this research. We further acknowledge the use of Python libraries including Pandas, NumPy, PyTorch, Scikit-learn, XGBoost, Prophet, Statsmodels, and Matplotlib. Finally, we thank the reviewers and editor of the Journal of Open Source Software for their valuable feedback and guidance.

Potential Conflict Disclosure

The authors declare that they have no competing financial, personal, or professional interests related to this work.

License

MIT License.