# CityTFT: Temporal Fusion Transformer for Urban Building Energy Modeling

arXiv: https://arxiv.org/abs/2312.02375 · HTML: https://mlsanity.com/paper/2312.02375/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 4 Dec 2023.

## Builds on (most cited first, 2 of 2)

- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Using Bayesian deep learning approaches for uncertainty-aware building energy surrogate models](https://mlsanity.com/paper/2010.03029/) (2020)

## Built on by (most cited first, 2 of 2)

- [Impact of data for forecasting on performance of model predictive control in buildings with smart energy storage](https://mlsanity.com/paper/2402.12539/) (2024)
- [SysCaps: Language Interfaces for Simulation Surrogates of Complex Systems](https://mlsanity.com/paper/2405.19653/) (2024)

## Abstract (arXiv)

Urban Building Energy Modeling (UBEM) is an emerging method to investigate urban design and energy systems against the increasing energy demand at urban and neighborhood levels. However, current UBEM methods are mostly physic-based and time-consuming in multiple climate change scenarios. This work proposes CityTFT, a data-driven UBEM framework, to accurately model the energy demands in urban environments. With the empowerment of the underlying TFT framework and an augmented loss function, CityTFT could predict heating and cooling triggers in unseen climate dynamics with an F1 score of 99.98 % while RMSE of loads of 13.57 kWh.