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