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.

翻译：城市建筑能耗建模（UBEM）是一种新兴方法，用于研究城市设计及能源系统以应对日益增长的城市与社区层面能源需求。然而，当前UBEM方法多基于物理模型，在多种气候变化情景下计算耗时。本文提出数据驱动的UBEM框架CityTFT，可精确建模城市环境中的能源需求。通过底层TFT框架与增强损失函数的赋能，CityTFT在未知气候动态场景中预测供热与制冷触发事件的F1分数达99.98%，同时负荷均方根误差为13.57 kWh。