Abstract
To address the pressing issue of urbanization and its significant energy consumption, the development of urban building energy modeling (UBEM) is an ongoing mission. This research explores the integration of machine learning (ML) models into UBEM. through a comprehensive analysis of various deep learning approaches, this work aims to enhance our understanding of UBEM and pave the way for more in-depth ML-UBEM studies.
Recommended Citation
T. Y. Dai et al., "A Comparison of Different Deep Learning Model Architectures and Training Strategy for Urban Energy Modeling," BuildSys 2023 - Proceedings of the10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, pp. 316 - 317, Association for Computing Machinery (ACM), Nov 2023.
The definitive version is available at https://doi.org/10.1145/3600100.3626277
Department(s)
Biological Sciences
Publication Status
Open Access
Keywords and Phrases
LSTM; RNN; Transformer; Urban Building Energy Modeling
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Association for Computing Machinery, All rights reserved.
Publication Date
15 Nov 2023