@article{bibcite_36913,
  author = {Tianzhen Hong and Han Li},
  title = {Good practices for documenting AI-based studies on energy and buildings},
  abstract = {<p>Artificial intelligence has transformed building science research over the past decade, with applications spanning energy modeling, energy prediction, HVAC optimization and controls, fault detection, and occupancy modeling. However, many studies lack adequate documentation of datasets, algorithms, training procedures, and validation methods. Building science research faces additional challenges including inconsistent evaluation metrics, limited generalizability across building types, climates, and significant gaps between experimental studies and deployed systems. This communication provides practical guidance for good practices in documenting and publishing AI-based research following established standards from the computer science and machine learning communities. By adopting frameworks such as Datasheets for Datasets, Model Cards, and standardized reproducibility checklists, researchers can ensure their work meets the rigorous documentation standards necessary for reproducible, comparable, and impactful building science research.</p>},
  year = {2026},
  booktitle = {Energy and Buildings},
  journal = {Energy and Buildings},
  series = {Energy and Buildings},
  volume = {355},
  pages = {117043},
  month = {03/2026},
  institution = {Elsevier BV},
  publisher = {Elsevier BV},
  issn = {0378-7788},
  url = {https://doi.org/10.1016/j.enbuild.2026.117043},
  doi = {10.1016/j.enbuild.2026.117043},
}