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, the RACE for 2030 Scholarship, and the NSW Decarbonisation Innovation Hub (NSW Decarb Hub). We also acknowledge Solcast and the Australian Bureau of Meteorology (BOM) for providing access to historical weather datasets for this research. We acknowledge the Australian Centre for Advanced Photovoltaics (ACAP), Energy Data Platform (ACAP-EDP) for the household net load data compiled from Solar Analytics and Wattwatchers. The North American dataset is derived from the UMass Smart* SunDance release (Chen and Irwin, 2017), used under CC BY 4.0, and the Belgian dataset from Fluvius Open Data (dataset 1_50, last updated by Fluvius on 2025-09-29), used under the Fluvius open data licence, with weather from the Open-Meteo historical weather API (CC BY 4.0). 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.