The development of deep learning in building energy: A bibliometric analysis
- DOI
- 10.6084/m9.figshare.33941790.v1
- Published
- 2026
- Container
- Not recorded
- Publisher
- Taylor & Francis
- Open access
- yes
Credibility signals
limited evidence Score 47/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
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Cite this work
BibTeX
@article{allodium:10.6084/m9.figshare.33941790.v1,
title = {The development of deep learning in building energy: A bibliometric analysis},
author = {Wenheng Zheng and Xiaojun Liang and Huilin Guo and Jinrui Zhou and Yuncheng Lan and Jianbo Su},
year = {2026},
doi = {10.6084/m9.figshare.33941790.v1},
url = {https://doi.org/10.6084/m9.figshare.33941790.v1}
}RIS
TY - JOUR TI - The development of deep learning in building energy: A bibliometric analysis AU - Wenheng Zheng AU - Xiaojun Liang AU - Huilin Guo AU - Jinrui Zhou AU - Yuncheng Lan AU - Jianbo Su PY - 2026 DO - 10.6084/m9.figshare.33941790.v1 UR - https://doi.org/10.6084/m9.figshare.33941790.v1 ER -
APA
Zheng, W., Liang, X., Guo, H., Zhou, J., Lan, Y., & Su, J. (2026). The development of deep learning in building energy: A bibliometric analysis. https://doi.org/10.6084/m9.figshare.33941790.v1
Source records
- datacite · retrieved 2026-09-25T10:50:49.214Z