As the global population and urbanization expand, the building sector has
emerged as the predominant energy consumer and carbon emission contributor. The
need for innovative Urban Building Energy Modeling grows, yet existing building
archetypes often fail to capture the unique attributes of local buildings and
the nuanced distinctions between different cities, jeopardizing the precision
of energy modeling. This paper presents an alternative tool employing
self-supervised learning to distill complex geometric data into representative,
locale-specific archetypes. This study attempts to foster a new paradigm of
interaction with built environments, incorporating local parameters to conduct
bespoke energy simulations at the community level. The catered archetypes can
augment the precision and applicability of energy consumption modeling at
different scales across diverse building inventories. This tool provides a
potential solution that encourages the exploration of emerging local ecologies.
By integrating building envelope characteristics and cultural granularity into
the building archetype generation process, we seek a future where architecture
and urban design are intricately interwoven with the energy sector in shaping
our built environments.

通过自我监督学习并融入建筑外观特征和文化细粒度，本研究提出一种新型工具，以地域特点为基础生成适用于不同建筑清单的代表性建筑原型，可提高能源模拟的精确性和适用性，从而鼓励探索新兴的本地生态系统。

城市生态编码：通过自监督学习实现能源模型自动生成的建筑范式

Encoding Urban Ecologies: Automated Building Archetype Generation  through Self-Supervised Learning for Energy Modeling

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.

提出了一种基于数据驱动的城市建筑能源模型（UBEM）框架 CityTFT，利用 TFT 框架和增强的损失函数准确预测城市环境中的能源需求，其 F1 得分达到 99.98% 且负荷的 RMSE 为 13.57 kWh。

CityTFT：城市建筑能源建模的时间融合变压器

CityTFT: Temporal Fusion Transformer for Urban Building Energy Modeling

Building archetypes, representative models of building stock, are crucial for
precise energy simulations in Urban Building Energy Modeling. The current
widely adopted building archetypes are developed on a nationwide scale,
potentially neglecting the impact of local buildings' geometric specificities.
We present Multi-scale Archetype Representation Learning (MARL), an approach
that leverages representation learning to extract geometric features from a
specific building stock. Built upon VQ-AE, MARL encodes building footprints and
purifies geometric information into latent vectors constrained by multiple
architectural downstream tasks. These tailored representations are proven
valuable for further clustering and building energy modeling. The advantages of
our algorithm are its adaptability with respect to the different building
footprint sizes, the ability for automatic generation across multi-scale
regions, and the preservation of geometric features across neighborhoods and
local ecologies. In our study spanning five regions in LA County, we show MARL
surpasses both conventional and VQ-AE extracted archetypes in performance.
Results demonstrate that geometric feature embeddings significantly improve the
accuracy and reliability of energy consumption estimates. Code, dataset and
trained models are publicly available:
this https URL

建筑原型、能源模拟、城市建筑能源建模、多尺度原型表示学习和几何特征是本研究的关键词。本文提出了一种基于多尺度原型表示学习的方法来提取具体建筑库的几何特征，并通过多个建筑下游任务约束潜在向量，这些定制化的表示在进一步的聚类和建筑能源建模中被证明是有价值的。在洛杉矶县的五个地区的研究中，我们展示了该方法在性能上超过了传统方法和仅使用 VQ-AE 提取的原型，并证明了几何特征嵌入能够显著提高能耗估计的准确性和可靠性。