Here, the urban microclimate is essential for accurate simulation-based urban building energy modelling (UBEM). However, a high spatial-resolution microclimate can increase the computational resources demands of UBEM. Surrogate modelling is one of the promising approaches for fast UBEM. This study proposes a bidirectional Long Short-Term Memory (LSTM)-based approach for simulation-based UBEM surrogate modelling. The estimations are aggregated into census tracts using total building floor area. A case study using UBEM to estimate annual hourly building energy use and anthropogenic heat from all existing buildings in Los Angeles County found that most of the surrogate models can complete the annual hourly simulation within 90 minutes with a normalized mean absolute error lower than 10%, and that the bidirectional LSTM outperforms the standard LSTM in accuracy. This study demonstrates the advantages of bidirectional RNN architecture in building energy surrogate modelling and is expected to promote long-term and high-resolution UBEM with detailed microclimates.
Pan, Xiyu, et al. "Surrogate modelling for urban building energy simulation based on the bidirectional long short-term memory model." Journal of Building Performance Simulation, May. 2024. https://doi.org/10.1080/19401493.2024.2359985
Pan, Xiyu, Xu, Yujie, & Hong, Tianzhen (2024). Surrogate modelling for urban building energy simulation based on the bidirectional long short-term memory model. Journal of Building Performance Simulation. https://doi.org/10.1080/19401493.2024.2359985
Pan, Xiyu, Xu, Yujie, and Hong, Tianzhen, "Surrogate modelling for urban building energy simulation based on the bidirectional long short-term memory model," Journal of Building Performance Simulation (2024), https://doi.org/10.1080/19401493.2024.2359985
@article{osti_2377673,
author = {Pan, Xiyu and Xu, Yujie and Hong, Tianzhen},
title = {Surrogate modelling for urban building energy simulation based on the bidirectional long short-term memory model},
annote = {Here, the urban microclimate is essential for accurate simulation-based urban building energy modelling (UBEM). However, a high spatial-resolution microclimate can increase the computational resources demands of UBEM. Surrogate modelling is one of the promising approaches for fast UBEM. This study proposes a bidirectional Long Short-Term Memory (LSTM)-based approach for simulation-based UBEM surrogate modelling. The estimations are aggregated into census tracts using total building floor area. A case study using UBEM to estimate annual hourly building energy use and anthropogenic heat from all existing buildings in Los Angeles County found that most of the surrogate models can complete the annual hourly simulation within 90 minutes with a normalized mean absolute error lower than 10%, and that the bidirectional LSTM outperforms the standard LSTM in accuracy. This study demonstrates the advantages of bidirectional RNN architecture in building energy surrogate modelling and is expected to promote long-term and high-resolution UBEM with detailed microclimates.},
doi = {10.1080/19401493.2024.2359985},
url = {https://www.osti.gov/biblio/2377673},
journal = {Journal of Building Performance Simulation},
issn = {ISSN 1940-1493},
place = {United States},
publisher = {Taylor & Francis},
year = {2024},
month = {05}}
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)https://doi.org/10.3115/v1/P15-1166