CityTFT: A temporal fusion transformer-based surrogate model for urban building energy modeling

Ting-Yu Dai, Dev Niyogi and Zoltan Nagy

Applied Energy, 2025, vol. 389, issue C, No S0306261925004428

Abstract: Almost one-third of global greenhouse gas emissions are from buildings and nearly 70 % of energy is consumed in urban areas. To address this as a long-term issue, accurately predicting building energy use at the urban level is indispensable. Urban Building Energy Modeling (UBEM) is an emerging method to investigate urban design and energy systems at urban and neighborhood levels. UBEM methods are considered reasonably accurate in simulating the performance of almost any building combinations. However, customized urban projects, and optimization problems involving many UBEM scenarios, are time-consuming and labor-intensive, and based on the scalability, the simulation runtime can be exponentially high if a broad set of design variations is analyzed. Here, we propose a data-driven approach to generate a surrogate model for UBEM and accelerate the simulation process. Based on the extensively used forecasting model, Temporal Fusion Transformer (TFT), we extract the static covariate encoder and variable selection network from the TFT structure while adding a small neural network to model the probability of triggering heating and cooling needs. The major advantages compared to other surrogate models are: (i) A sequential input in the transformer-based model to improve the temporal accuracy. (ii) A physics-inspired training strategy that builds on weather dynamics and urban interactions for hourly energy demands concurrently with a customized loss function. (iii) Improved generalizability for the proposed surrogate model using open and public data. In our study of 114 buildings in different climate zones in the US, our model predicts heating and cooling triggers in unseen climate dynamics with an F1 score of 0.83 while the root mean squared error (RMSE) of hourly loads was 71.51 kWh. This model is available for interfacing with climate projections and city-scale analysis as part of atmospheric urban digital twins.

Keywords: Urban building energy modeling; Machine learning; Temporal fusion transformer (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261925004428
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:389:y:2025:i:c:s0306261925004428

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2025.125712

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().