Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts
- DOI
- 10.3390/buildings14020371
- Published
- 01
- Container
- Buildings
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/buildings14020371,
title = {Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts},
author = {Mengjie Han and Ilkim Canli and Juveria Shah and Xingxing Zhang and Ipek Gursel Dino and Sinan Kalkan},
year = {2024},
journal = {Buildings},
doi = {10.3390/buildings14020371},
url = {https://doi.org/10.3390/buildings14020371}
}RIS
TY - JOUR TI - Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts AU - Mengjie Han AU - Ilkim Canli AU - Juveria Shah AU - Xingxing Zhang AU - Ipek Gursel Dino AU - Sinan Kalkan PY - 2024 JO - Buildings DO - 10.3390/buildings14020371 UR - https://doi.org/10.3390/buildings14020371 ER -
APA
Han, M., Canli, I., Shah, J., Zhang, X., Dino, I. G., & Kalkan, S. (2024). Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts. Buildings. https://doi.org/10.3390/buildings14020371
Source records
- doaj · retrieved 2026-09-26T09:00:17.795Z