Surrogate modelling for urban building energy simulation based on the bidirectional long short-term memory model

Abstract

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.

KEYWORDS:

• Bidirectional LSTM model
• anthropogenic heat
• building stock
• energy use
• surrogate model
• machine learning

Acknowledgments

Authors thank Dr. Pouya Vahmani for the microclimate data produced using the WRF programme.

Data availability statement

The data that support the findings of this study are openly available in Github at https://github.com/pppxiyu/SurUBEM.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Building Technologies of the U.S. Department of Energy: [grant no DE-AC02-05CH11231].